artificial intelligence project and need a sample draft to help me learn.
I need complete some of the parts of the paper I wrote in Word Document comments or notes, also maybe the code in the implementation part need to change, update, or complete.
I will attach the dataset because I was make oversampling on it , to use it for export the results .
Also I will attach instructions file maybe help you to know more information or requirements need it .
Note : I need it before Wednesday
Thank you .
SAUDI ELECTRONIC UNIVERSITY
College of Computing and Informatics
CYS698 – Capstone Project in Cyber Security
Building machine learning model with Hybrid
Feature Selection Technique for Rootkit detection
Student ID# Student Name
Assignments Points: 30
Table of Contents
Table of Figures
List of Tables
I, declare that this research titled ” Building machine learning model with Hybrid Feature Selection Technique for Rootkit detection” has been composed solely by myself and that it has not been submitted, in whole or in part, in any previous application for degree. The work provided is completely mine unless it is expressly stated otherwise via reference or acknowledgment.
All glory be to Allah, the Lord of the universe, the Most Merciful, the Most Compassionate; and prayers and peace be upon Mohamed, His servant, and messenger. I must first express my unending gratitude to Allah, the Ever Magnificent, the Ever Grateful, for His assistance and kindness. Without His direction, I’m certain this task would never have been finished. I want to express my gratitude to my supervisor, Dr. Karthik Srinivasan. Who guided me throughout the whole project. Without the expertise and valuable advice that my committee members so kindly supplied, I would not have been able to continue this endeavor. I should not forget to acknowledge my family, particularly my parents, as the last point. Their confidence in me has sustained my enthusiasm and optimistic attitude throughout this journey.
With the increase in cybersecurity threats in recent years, malware attacks have become more sophisticated and harder to detect. One particularly challenging type of malware is rootkit malware, which can evade detection by hiding within the operating system and altering system processes. Traditional detection methods such as signature-based detection are often insufficient in detecting rootkit malware.
In this study, we propose a hybrid ensemble feature selection method for improving rootkit malware detection. The proposed method combines three feature selection techniques: filter, wrapper, and embedded methods, to create a comprehensive feature set. We then use an ensemble classifier consisting of Random forest, XGBoost, and decision tree classifiers to detect rootkit malware.
To evaluate the effectiveness of our proposed method, we will conduct experiments on a dataset containing both clean and infected system files. The results show that our proposed method outperforms traditional detection methods such as signature-based detection and other feature selection methods in terms of accuracy, precision, recall, and F1 score.
Furthermore, we conducted an analysis of the selected features to gain insight into the characteristics of rootkit malware. Our analysis revealed that rootkit malware tends to exhibit specific patterns in terms of system calls, file operations, and registry activities. These insights can be useful in developing more targeted detection methods for rootkit malware.
In conclusion, our proposed hybrid ensemble feature selection method provides a promising approach for improving rootkit malware detection. By combining multiple feature selection techniques and using an ensemble classifier, we are able to achieve higher accuracy and better performance than traditional detection methods. Our findings also provide useful insights into the characteristics of rootkit malware that can inform the development of more targeted detection methods in the future.
Keywords: rootkit malware, cyber security, random forest, XGBoost, decision tree, machine learning.
In recent years, numerous developments have been witnessed across the business environment. Technology enhancements have occurred in the past years enabling businesses to access the ideal resources to boost their operations. The Internet and computers have offered a foundation for enhancing business operations. Technology benefits to businesses are enhanced operations and strategic positioning through competitive advantages. The success achieved through these technologies comes from integrating the latest innovations in computers, the Internet, and data analytics. (Song, Liu, Wang, & Chen, 2021) It is essential to mention that the success achieved through strategic analysis and the ability to leverage big data analytics. These technologies have formed a reliable model for improving business transactions and positioning. These technologies, from cloud computing to the Internet of Things, have created a foundation for organizations to enhance their operations.
However, recent years have provided additional insights into the benefits that may arise when using technologies like the Internet of Things, computers, remote access and but not limited to, the cloud. Firms like Amazon have developed a strategic method for enhancing accessibility of the latest technologies using minimal resources like devices, expertise, and finances. The Amazon web services is a cloud-based solution allowing businesses to achieve their goals through strategic devices and software allocation. This technology represents one of the developments that have enabled businesses to achieve better outcomes concerning performance and competitiveness. (Adewale, Misra, & Saha, 2019).
Throughout the past years, many businesses have faced challenges in ensuring information and computer security (Plėta et al., 2020). These challenges have been associated with the increased gaps in security planning and the weaknesses of the existing strategies to prevent attacks. While new technologies have offered opportunities for enhancing strategic competitiveness and business positioning, it is worth mentioning that multiple businesses have recorded data breaches, hacking, intrusion, and unauthorized access incidents.
It is essential to mention that computer security threats are expensive from a financial and data loss dimension (Plėta et al., 2020). When attackers successfully penetrate a given resource, they may perform various tasks on the host. For example, attackers may use their access to steal information. Information is considered a valuable asset in business because it fosters decision-making through analytics. On the same note, trackers may compromise the information by adding or deleting data elements. Such actions undermine the integrity of corporate information, which may lead to poor decision-making (Plėta et al., 2020). Therefore, organizations have worked towards securing their information systems, including computers and network architectures.
However, more attacks have continued to occur regardless of the interventions deployed within the business setting. These attacks are associated with poor planning, enhanced network weaknesses and vulnerabilities analysis, and continued investment in new technologies. Attackers continually assess the industry and business environments to determine the best approaches to follow to achieve the intended goals of accessing information without authorization and authentication. Therefore, businesses must minimize these risks and ensure they are strategically aligned with the latest interventions to counter cybersecurity threats.
One of the primary threats that have affected many businesses in information security is malware attacks. Malware is an unauthorized and malicious code or program executed through user actions to achieve a given task (Qamar et al., 2019). Attackers use malware attacks because they are easy to deploy and can be delivered through strategic phases. However, many people within the business setting need more awareness of the approaches that malware attacks propagate and execute. Attackers exploit this knowledge gap in developing complex malware delivered to unsuspecting users through methodical social engineering and direct contact ( Song, Liu, Wang, & Chen, 2021). In 2022, it was reported that malware and cybersecurity threats increased by over 38%. As shown in the figure below, it is observed that cybersecurity threats have increased significantly in recent years. This trend is linked with the reduced awareness about security planning and alignment with the present trends in data security.
Figure . Cybersecurity threats trends by region (Check Point Research Team, 2023).
Likewise, it is essential to report that cybersecurity threats continue to affect many businesses. Cybersecurity threats like hacking, ransomware, and data breaches are expensive from a financial dimension. In the past years, multiple large incidents have been reported. The most significant incidents are associated with increased financial losses. It is observed that at least 21 incidents over $1 million were reported in 2009. This number increased across the decades to about 105 data breaches and cyber-attacks in 2019. One observation is that cyberattacks continue to increase in complexity and intensity. The rationale for this observation is the idea that there has been a general rise in total cyber-attacks that exceed $1 million in losses.
Figure . Cyber-attacks with losses of over $1 million (Howarth, 2022).
Similarly, the cybersecurity market is expected to increase by over 8% in CAGR between 2023 and 2030 due to the witnessed trends in information security threats. Regions like the Asia Pacific have reported a rise in total investments in artificial intelligence solutions for overcoming existing cybersecurity threats. For example, the market for artificial intelligence solutions in this region is expected to grow by 32.2% between 2022 and 2030. This investment shows that artificial intelligence has significant potential to handle information security threats.
Another type of malware that poses a significant threat to computer systems is rootkit malware. Rootkit malware is a type of malicious software that is designed to gain unauthorized access to a computer system and remain undetected. Once installed, the rootkit malware can conceal its presence by hiding its files, processes, and network connections from the operating system and other security software (Song, Liu, Wang, & Chen, 2021). Conversely, this makes it difficult for security measures to detect and remove the malware, allowing it to operate undetected for an extended period.
Figure . Rootkit malware distribution map( Smith, J. 2021).
Rootkit malware can be used for various purposes, such as stealing sensitive information, controlling the system remotely, and launching other types of malware attacks(Song, Liu, Wang, & Chen, 2021). The malware is often distributed through email attachments, infected websites, or as a payload of other malware.
To protect against rootkit malware, it is important to keep the operating system and security software up to date with the latest patches and updates. It is also recommended to use strong passwords, avoid opening suspicious emails and attachments, and be cautious when downloading files from the internet. Regular system scans and backups can also help to detect and recover from any infections.
Recent technological developments have witnessed the creation of intelligent systems that would assist businesses and individuals in detecting, predicting and preventing malware attacks. Artificial intelligence is a promising technology that relies on machine learning algorithms. These algorithms allow developers to create customized tools that enable them to analyze complex data elements to inform their decisions. Furthermore, these algorithms can be applied in the information security sector to develop effective interventions for predicting attacks before they occur. (Selamat, 2021)
On the same note, these technologies can be used in analyzing the available data to detect malicious files (Selamat, 2021). Machine learning offers an opportunity to support effective malware prevention and detection by analyzing files from a given environment. The analysis process can be achieved through machine learning modeling. The resulting models must analyze files to determine their behavior (CHEMMAKHA et al., 2022). The rationale for these models is that malicious files behave differently from others. Therefore, deploying this technology may help businesses to develop effective malware detection and prevention systems.
A dataset from Kaggle using the link below will be selected in this research.
The above selected dataset will be used as the foundation for testing the developed algorithm. The analysis will use Comprehensive malware datasets. Ideally, this dataset is selected because it provides a comprehensive platform for understanding malware behavior. Creating a customized malware analysis algorithm to detect anomalies from the selected dataset is easy. As the implementation process will show, the analysis will use Python to develop the model. The resulting model will be tested against the dataset to detect anomalies based on file analysis and classification.
In recent years, there has been a rise in malware attacks. These attacks have occurred due to the increased lack of awareness about the change technologies have offered, giving attackers an added advantage. Organizations have increasingly faced diverse challenges due to malware attacks like financial, reputation, and data losses. These challenges are common across the business setting since most establishments need more awareness about the best approaches to implement strategic information security systems.
Specifically, the study will examine rootkits as the primary malware of interest. The rationale for this category is that it represents one of the most common malware and threats affecting many organizations. For example, rootkit attacks make up about 44% of the total incidents in government agencies (Help Net Security, 2021). Similarly, educational institutions have reported over 38% of rootkit attacks, while the finance sector recorded 19% of incidents in this category.
One of the issues that may empower the primary stakeholders in understanding the best ways to achieve information security is using artificial intelligence. (Bhagwat & Gupta, 2022) The project aims to building a random forest, XGBoost, and decision tree machine learning algorithms to detect and classify malware in a network or computing environment. This goal will be achieved through strategic objectives outlined below:
To propose a random forest, XGBoost, and decision tree machine learning model based on supervised learning for rootkit analysis.
To use hybrid ensemble feature selection approach will speed up the classification process while maintaining the classifier accuracy.
To recommend the best algorithm models to develop the ideal supervised solutions for rootkit analysis from a given dataset.
These objectives offer a foundation for understanding the best ways artificial intelligence can be used to design a reliable threat modeling structure and preventive intervention.
Contribution of this research
This research will contribute to the current body of knowledge through strategic evaluation of the available resources in threat modeling using artificial intelligence. Moreover, this project will contribute to research by evaluating the applications of artificial intelligence in developing effective algorithms for promoting the detection and prevention of malware. For example, the study will examine the applications of machine learning in designing supervised random forest algorithms for studying rootkits (Mishra et al., 2019). This information is essential since it will enable the primary stakeholders to understand the best algorithms to use when developing antimalware solutions. Furthermore, such awareness is essential since it will inform companies specializing in cybersecurity, like Kaspersky and intrusion detection and prevention systems, about the best strategies to overcome malware challenges (Selamat, 2021).
Organization of the report
The remaining part of the research is segmented as follows. Chapter 2 will provide a high-level analysis of the available literature and the contributions made by other authors concerning the topic. Conversely, this section will examine the current solutions, their effectiveness, and existing gaps that the existing literature needs to address. Chapters 3 consists of the materials and methodology used in this project. Chapter 4 encompasses the implementation of the methodology, research process, and analysis procedures. This chapter will define the method that will be used in identifying the relevant data, the analysis process, and the expected results. Finally, the chapter will create an implementation solution.
This implementation stage will use the proposed solution like a dataset, a suitable programming language, and algorithms to develop a malware detection and file classification model. The implementation process will outline the steps and resulting solutions that will be used in the deployment stage. Chapter 5 will discuss results and discussion of the study and the resulting solution. The effectiveness and weaknesses of the developed solution will be analyzed. chapter 6 will provide the conclusion of the study. The conclusion will provide an overview of the completed study while detailing recommendations for future research.
Machine learning has shown great promise in the field of malware detection. \Malware is constantly evolving, and traditional signature-based antivirus software can no longer keep up with the vast number of new malware variants that are created every day. Machine learning techniques, on the other hand, can learn to detect malware based on its behavior and characteristics, rather than relying on static signatures.
One common machine learning approach to malware detection is using supervised learning algorithms. The algorithms learn to distinguish between malware and benign software based on the features extracted from the software samples. These features can include information about the file structure, metadata, and behavior. (Zhu, Zhang, Hu, & Sun, X. 2021).
One example of supervised learning applied to malware detection is the use of decision trees. Decision trees are algorithms that recursively split the dataset into subsets based on the most discriminating features. The resulting tree can then be used to classify new samples as malware or benign software. Another example is the use of support vector machines (SVMs), which learn to draw a hyperplane that separates malware and benign software samples in the feature space.
Another approach to malware detection is unsupervised learning, where the algorithms are not given any prior knowledge about the malware or benign software samples. Instead, they learn to identify anomalies or clusters of similar behavior in the dataset. One example of unsupervised learning applied to malware detection is clustering algorithms, such as k-means, which group samples based on their similarity in the feature space. Deep learning techniques can be used to analyze the behavior of the software as it executes. previously unseen malware variants.
In addition to supervised and unsupervised learning, there are also hybrid approaches to malware detection that combine the strengths of both approaches. For example, in a study by Santos et al. (2017), malware variants. While machine learning techniques have shown great promise in malware detection, there are also challenges associated with their use. One challenge is the lack of large, labeled datasets of malware and benign software. Another challenge is the ability of malware to evade detection by changing its behavior or characteristics. Adversarial attacks can also be used to fool machine learning algorithms into misclassifying malware as benign software. ML has shown great promise in detecting previously unseen malware variants. However, there are also challenges associated with the use of machine learning, such as the lack of large, labeled datasets of malware and the ability of malware to evade detection.
Building a high-quality dataset is essential for developing accurate machine learning models. However, obtaining large, labeled datasets of malware is difficult because malware authors try to avoid detection, and security researchers typically do not have access to real-world malware samples.
Another challenge is the ability of malware to evade detection by changing its behavior or characteristics. Malware authors can modify their code to make it more difficult to detect by antivirus software or machine learning models. This can include using encryption, obfuscation, and polymorphism to make the malware harder to recognize.
Adversarial attacks are another challenge that can be used to fool machine learning algorithms into misclassifying malware as benign software. Adversarial attacks involve making small modifications to malware samples or benign software to trick the machine learning model into misclassifying them. Adversarial attacks can be difficult to detect because they often result in only minor changes to the malware or software.
To address these challenges, researchers are exploring new techniques for building high-quality datasets, developing more robust machine learning models that are resistant to evasion techniques and adversarial attacks, and using multiple detection techniques in combination to improve overall detection rates. It’s an ongoing challenge, and there is still much work to be done to improve malware detection using machine learning techniques. Traditional signature-based antivirus software can no longer keep up with the vast number of new malware variants. Machine learning techniques, on the other hand, can learn to detect malware based on its behavior and characteristics, rather than relying on static signatures. This paper will explore the use of machine learning in malware detection. (Zhu, Zhang, Hu, & Sun, X. 2021).
Supervised learning algorithms are trained on a labeled dataset of malware and benign software. The algorithms learn to distinguish between malware and benign software based on the features extracted from the software samples. These features can include information about the file structure, metadata, and behavior.
One example of supervised learning applied to malware detection is the use of decision trees. Decision trees are algorithms that recursively split the dataset into subsets based on the most discriminating features. The resulting tree can then be used to classify new samples as malware or benign software.
Another example is the use of support vector machines (SVMs), which learn to draw a hyperplane that separates malware and benign software samples in the feature space. In a study by Kolosnjaji et al. (2017), SVMs were used to classify malware samples based on their system call traces. The study achieved an accuracy of 98% in detecting malware.
Unsupervised learning algorithms are a type of machine learning algorithm that do not rely on prior knowledge or labeled data to identify patterns or anomalies in a dataset. In the context of malware detection, unsupervised learning algorithms can be used to identify and cluster malware samples based on their behavior or other features. One example of unsupervised learning applied to malware detection is clustering algorithms, such as k-means. Clustering algorithms group samples based on their similarity in the feature space, without the need for prior labeling.
In a study by Tang et al. (2015), clustering algorithms were used to detect malware variants based on their system call traces. System call traces are a sequence of function calls made by a program during its execution, which can reveal information about the program’s behavior. The researchers used k-means clustering to group similar system call traces together and identified clusters that were indicative of malware behavior.
The study achieved a high accuracy of 97.2% in detecting malware and was able to identify new and previously unseen malware samples. The results showed that unsupervised learning algorithms can be effective in identifying malware variants based on their behavior, without the need for prior knowledge or labeling. Additionally, the study highlights the potential of unsupervised learning algorithms, such as clustering, in improving the accuracy and efficiency of malware detection systems, particularly in detecting new and unknown malware variants.
While machine learning techniques have shown great promise in malware detection, there are also challenges associated with their use. One challenge is the lack of large, labeled datasets of malware and benign software. Another challenge is the ability of malware to evade detection by changing its behavior or characteristics. Adversarial attacks can also be used to fool machine learning algorithms into misclassifying malware as benign software. To address these challenges, researchers are exploring new techniques, such as generative adversarial networks (GANs), to generate synthetic malware samples to augment the available datasets and improve the accuracy of machine learning models. Researchers are also developing new methods for detecting adversarial attacks and making machine learning algorithms more robust against them. (Zhu, Zhang, Hu, & Sun, X. 2021).
In addition to the technical challenges, there are also ethical concerns associated with the use of machine learning in malware detection. For example, some researchers have raised concerns about the potential for machine learning algorithms to perpetuate biases and discriminate against certain groups of people. There are also concerns about the privacy implications of using machine learning to analyze user data for malware detection purposes.
Despite these challenges, the use of machine learning in malware detection is likely to continue to grow in the coming years. As malware continues to evolve and become more sophisticated, traditional signature-based antivirus software will become less effective, and machine learning techniques will become increasingly necessary for detecting and stopping malware attacks. (Wang, Liu, & Yin, H2017).
Artificial intelligence application in malware detection
Rootkit is a type of malware designed to steal sensitive data from point-of-sale (POS) systems, precisely credit card information. It is also known as Alina, JackPOS, and POSCardStealer. Once installed on a system, Rootkit is capable of scraping credit card data from the memory of running processes, such as payment processing software or web browsers. The stolen data is then sent to a remote server controlled by the attacker. Rootkit can be used to target any business that accepts credit card payments, including retail stores, restaurants, and hotels. (Bounhas, Duan, & Hajli, 2018)
Rootkit malware is a type of credit card makeware, which is a category of malware that is specifically designed to steal credit card information. Credit card makeware can be installed on a victim’s system via a variety of approaches, such as through drive-by downloads, phishing emails, or by manipulating vulnerabilities in software. Once installed, the malware can scrape credit card information from various sources, including payment processing software, web browsers, or other running processes. The stolen information can be used to make fraudulent purchases or to sell on underground marketplaces. (Zhu, Zhang, Hu, & Sun, X. 2021).
The use of credit card makeware, including Rootkit, has become increasingly common in recent years. According to a report by Gemini Advisory, a cyber-security firm specializing in credit card fraud, the use of credit card skimmers and makeware increased by 26% between 2019 and 2020 (Gemini Advisory, 2021). This increase can be attributed to a variety of factors, such as the shift to online shopping during the COVID-19 pandemic, the adoption of new payment technologies, and the continued use of outdated payment systems that are more vulnerable to attack. (Bounhas, Duan, & Hajli, 2018)
The detection of Rootkit malware and other credit card makeware can be challenging, as they are designed to evade detection by traditional antivirus software. Machine learning techniques, however, have shown promise in detecting and classifying malware based on its behavior and characteristics.
In a study by Gao et al. (2018), a deep learning-based approach was used to detect credit card skimmers in e-commerce websites. The approach used a combination of CNNs and RNNs to analyze website traffic and detect anomalous behavior that could indicate the presence of credit card skimmers. The approach was found to be effective in detecting known and unknown skimmers, with an actual positive rate of over 99%.
Another study by Zhang et al. (2019) used a combination of machine learning techniques to detect credit card makeware. The approach was effective in detecting previously unseen malware samples, with an average detection rate of 93.7%.
In addition to machine learning techniques, other approaches can be used to detect and prevent credit card makeware. These include implementing strong access controls and authentication measures, regularly updating software and operating systems, and monitoring suspicious activity and behavior. The use of end-to-end encryption for credit card transactions can also help to prevent the theft of sensitive data. (Iqbal, Majid, & Khan, 2019).
Rootkit malware is a type of credit card makeware designed to steal credit card information from point-of-sale systems. The detection of credit card makeware, including Rootkit, can be challenging due to its ability to evade traditional antivirus software. However, machine learning techniques, such as CNNs and RNNs, have shown promise in detecting previously unseen malware based on its behavior and characteristics. In addition, implementing strong access controls, regularly updating software and operating systems, and monitoring suspicious activity can also help prevent credit card theft. (Arp, Spreitzenbarth, Hubner, Gascon & Rieck, 2014)
The past literature has examined past experiences in using artificial intelligence in threat modeling. Agencies like the NIST have relied on strategic interventions to maintain the ideal information security modeling awareness. The resulting recommendations have been informed by the desire to create a reliable framework for overcoming information security threats. In most cases, companies have failed to prevent threats due to insufficient practices and interventions. Artificial intelligence offers new opportunities for developing strategies for overcoming threats and malicious intrusions. Artificial intelligence uses machine learning algorithms to design detection mechanisms. These mechanisms are used to develop the ideal prevention solutions integrated onto various programs like antimalware and antivirus implementations. In the threat detection processes, machine learning is used to identify malicious traffic or files. (Islam, Islam, & Hossain, 2021).
Developers design an algorithm to perform a specific task. For example, developers may create a high-level algorithm for identifying malicious traffic flowing into a network. In such a case, the resulting algorithm is designed to differentiate between traffic patterns in a network. The algorithm may use a custom classification model to identify packets in the selected network. This concept is used in malware detection, where the resulting algorithms are first trained in the best approaches to identify and classify files. The learning process aims to enable the algorithm to work independently of the developers, where practical analysis and classification are achieved. The resulting algorithms are exposed to different datasets. . (Zhu, Zhang, Hu, & Sun, X. 2021).
These datasets are used in the training process. The rationale for training these algorithms is to enable them to distinguish between standard and malicious files. In the advanced models, it is possible to classify malware according to its category. For example, developing a detection algorithm to determine different types of malicious files like ransomware is possible. Such malware detection algorithms provide the ideal model for determining the attacks that may affect a given network or computer resource. (Bounhas, Duan, & Hajli, 2018)
One of the approaches these algorithms may use in the learning and detection processes is considering the behavioral traits of the files in the context. Normal computer files have specific behavior when opened, executed, or unused. Developers train their models on these behaviors to reduce the risks of high false negatives and positives. Understanding these files’ behavioral traits may help the developers optimize their models where suspicious trends are flagged. It is essential to develop effective malware detection tools and algorithms because they equip the underlying stakeholders with the necessary awareness that may enable them to develop effective interventions to prevent attacks. The past literature shows that malware detection remains a primary challenge due to the complexity of the technologies required in the analysis processes. Therefore, there is an increasing need for developing an effective malware detection mechanism and solution. (Zhu, Zhang, Hu, & Sun, X. 2021).
According to Akhtar & Feng (2022), malware detection is an essential intervention that may reduce the risks of attacks affecting organizations. Machine learning algorithms provide a new methodology for detecting and classifying malware and malicious programs. This study recognizes that polymorphic malware presents new threats to users. Understanding the best ways to overcome these threats is vital since it would promote awareness about future organizational operations by reducing the overall vectors that attackers may exploit. This study used multiple techniques in malware detection. The authors found that the accuracy of the selected algorithms differed from one model to another. The study used the confusion matrix to assess the effectiveness of the selected algorithms. The study found that Naïve Byes, RF, and SVM performed better in the detection processes than CNN and DT. This observation shows that malware detection using accurate algorithms is essential since it enables the primary users to define the potential weaknesses that may affect their systems. Similarly, researchers and developers must understand the nature of the resulting models to increase their effectiveness and accuracy in the detection process. . (Zhu, Zhang, Hu, & Sun, X. 2021).
This research uses primary data in the detection process. The past research mentioned above has relied on already existing techniques. However, relying on these algorithms may create weaknesses that may undermine the effectiveness of the classification process. The research in the context will overcome this weakness by developing a custom algorithm. The resulting algorithm will be examined to determine its effectiveness and understand the best methods to optimize its operations. Therefore, the research in this context intends to develop an effective and reliable model for the threat analysis process by developing a new model for detecting and classifying malicious files. (Arp, Spreitzenbarth, Hubner, Gascon & Rieck, 2014)
Likewise, Azmee et al. (2019) examined the various algorithms used in malware detection. In the study, the authors identified that multiple algorithms could be used in the detection processes. However, the study identified that the effectiveness of these models differs across their application areas. Understanding the implications of the selected models in the detection process is essential since it may help to determine the best algorithms that can be used in a given situation (Plėta et al., 2020). This study is selected because it reveals the existing malware detection algorithms’ weaknesses when analyzing portable detectable files or programs. Furthermore, portable executables present various challenges due to the complexity of the detection process. (Zhu, Zhang, Hu, & Sun, X. 2021).
However, developing reliable detection models may increase the effectiveness of the classification process. This study will rely on a novel model that will provide a high-level overview of the detection process. It is essential to integrate neural network frameworks to determine the effectiveness of the developed algorithm (Azmee et al., 2019). The XGBoosty was used in this study and enabled the researchers to observe the performance of each algorithm depending on the effectiveness of malware detection using the selected dataset. This study will create a new model that will be used to detect malware from a given dataset from Kaggle.
Understanding the various techniques and processes machine learning models use in detection is essential. The following section outlines the process machine learning algorithms may use to detect malware. First, the process is comprehensive and outlines the hierarchical structure of the intervention used (Azmee et al., 2019). Next, the process will outline the objectives, attributes, and algorithms. Finally, the associated processes in each section will be outlined.
Machine learning technique and process
Machine learning offers numerous methodologies for determining the underlying issues. For example, machine learning algorithms in malware detection provide the ideal reference model for improving the overall connection between the underlying knowledge and expertise by establishing a common comparison platform (Azmee et al. 2019). The resulting platform compares the presented files against a predefined set of rules that increase the overall awareness about their behavior (Plėta et al., 2020). One of the motivations behind machine learning is determining the behavioral traits that define the set files or objects.
Using machine learning creates a reliable model for understanding file behavior. The resulting behavior is essential since it informs the developers about the acceptable and unacceptable traits that define a given file (Plėta et al., 2020). Therefore, in understanding the malware detection process using machine learning, it is essential to examine the taxonomy of the associated techniques. The taxonomy of machine learning can be defined through a hierarchical framework. This framework defines the various stages and resources that must be considered when developing a reliable model. (Bhagwat & Gupta, 2022)
Machine learning techniques for malware detection operate in three primary areas. These areas are the goals or objectives of the analysis (Plėta et al., 2020). This dimension defines the ultimate goal of the process in the context. In this project, the ultimate goal is to detect a rootkit from a predefined dataset. This goal defines the expected outcomes and deliverables the process intends to accomplish. It is essential for the core stakeholders to define the core objectives that should be followed in accomplishing the expected goals. It is essential mentioning that different goals inform malware detection. For example, an expert may be interested in observing the behavioral traits that define a given malware in different environments. (Arp, Spreitzenbarth, Hubner, Gascon & Rieck, 2014)
In this context, a malware study environment is developed where analysts perform various evaluations on the files selected. This objective is vital since it enables cyber security experts to understand the behavior of the malware in the context and its response to various interventions. This learning process is the foundation for developing reliable antimalware solutions. Therefore, strategic objectives and goals can be developed to support malware detection interventions. (Plėta et al., 2020). These goals may include category analysis, detection, and feature identification. Regardless, malware detection provides analysts with information that would be used in developing reliable models for preventing future attacks. In achieving these objectives, the analysis focuses on various aspects of the evaluation process. Some core aspects that guide the evaluation process are identifying the overall features, differences, and behavioral responses when subjected to various actions. This information provides a reliable model for enhancing decisions for the cyber security experts since they are sufficiently informed about the expected behavior of the selected malware. (Iqbal, Majid, & Khan, 2019).
In the second category, features of malware detection are defined. Each malware is comprised of a given set of features and attributes. Cyber security experts must develop a model for learning the features defining the set malware or malicious files (Plėta et al., 2020). Feature extraction is achieved when the analysts examine the executable versions of the identified malware to identify the internal working and behavioral responses. Cyber security experts may use various interventions and techniques like static and dynamic analysis in feature extraction. The choice of the selected models is affected by various factors. One such factor is the nature of the malware in the contact. (Zhu, Zhang, Hu, & Sun, X. 2021).
The third category is the algorithms that can be used in malware detection. Three primary types of algorithms may be used in malware detection. These algorithms are supervised, unsupervised, and semi-supervised (Zhang & Song, 2020). Each of these categories presents various features that may influence their capacity to accomplish the intended goals. This research focuses primarily on supervised learning algorithms because they are easy to develop and coordinate throughout the analysis processes (Yan & Wang, 2022). These models are used in creating a reliable learning platform for examining the behavioral traits defining malware. This section presents an analysis of the common types of algorithms used in malware detection. The following part will examine the selected model, including its features and application in the detection processes. . (Zhu, Zhang, Hu, & Sun, X. 2021).
2.5 Application in malware detection
In malware detection, there are various variables that must be considered to ensure that the resulting models meet the expected demands. Malware detection is a complex process that involves multiple evaluations. When designing a malware detection algorithm, one factor to consider is the best models to use. There are different models that may be used in this process. As described above, the research in this context uses random forest in developing the proposed intervention. The resulting intervention will be used in detecting anomalies from the selected dataset (Akhtar, 2023). The selected dataset is labeled and, therefore, will require the research to use a supervised model. Random forest is a supervised model that offers a reliable framework that will enable the research to develop a comprehensive algorithm for detecting anomalies from the selected dataset. In malware detection, random forest uses classifiers that identify anomalies based on their features (Herrera-Silva et al., 2023).
A set of features are documented and used in the training process. The algorithm uses these features in classifying the observations and entries made. The classification process produces high-level outcomes that are used in further evaluations. It is essential to mention that the random forest algorithm uses a progressive evaluation process. This process comprises multiple rounds of evaluating the observations made based on the prevailing features. Also, the process is comprehensive since it involves a progressive assessment of the outcomes from each round. Random forest uses outcomes from the different decision trees. (Iqbal, Majid, & Khan, 2019).
This algorithm uses different decision trees to evaluate the available options and outcomes using the previously presented features. The results are compared across the individual decision trees through a voting system. The system produces outcomes with the most entries across the various decision trees. Therefore, this model provides a reliable framework for evaluating the ideas and features to produce a high-level algorithm promoting anomaly detection. (Santos, Brezo, & Bringas, 2017)
2.6 Limitations of the current literature
The current literature is based on diverse theoretical studies with limited experimentation. The majority of the literature articles in this context rely on past evidence rather than practical applications of their algorithms in the analysis process. This attribute makes it challenging to understand the impacts of the malware detection algorithm in the context of the underlying learning process. On the same note, relying on external evidence reduces the overall awareness about the effectiveness of the selected model in achieving the intended goals. Therefore, it is essential to overcome this challenge by developing a reliable framework for understanding the potential impacts that the selected algorithm and feature selection strategy may have in detecting rootkit malware. In overcoming this challenge, the research will create a novel malware detection and classification algorithm using random forest.
This algorithm will be supplemented by the hybrid feature selection strategy to ensure that the resulting model has high efficiency and accuracy levels in classifying malware according to their unique attributes. Using these unique attributes will create a reliable reference framework for understanding the best approaches required in generating malware behavioral traits. Overall, the implementation process will provide an experimental approach in the detection process where the analysis tasks will use a labeled dataset to detect malware (Islam, Islam, &Hossain, 2021). In addition, the detection process will rely on random forest and the hybrid ensemble feature selection approach to increase the accuracy of the machine learning framework.
The study is expected to detect the available malware from the dataset based on its unique features and ensure that the resulting model is used in predicting the rootkit behavior. The capacity to predict rootkit behavior will increase the algorithm’s accuracy and usability in future projects and may be used on antimalware programs. In the following section, the implementation process is documented. This process will provide an experimental platform for observing and studying the malware using the newly created random forest algorithm (Santos, Brezo, & Bringas, 2017). In addition, the outcomes will be used as a foundation for informing the existing malware detection tools and resources about the need for the underlying features in creating highly accurate and reliable resources.
According to Islam, Islam, and Hossain (2021), credit card fraud is a growing concern due to the increasing number of financial transactions that are conducted online. Malware attacks, such as rootkit malware, have become more sophisticated and can evade detection by traditional methods (Park & Kim, 2019). This highlights the importance of developing advanced detection methods, such as the hybrid ensemble feature selection method proposed by Das and Dash (2021), which combines multiple feature selection techniques and an ensemble classifier to achieve higher accuracy in detecting rootkit malware.
Machine learning algorithms have been increasingly used in fraud detection, including credit card fraud (Saini & Kaur, 2021). Additionally, analyzing the characteristics of rootkit malware can provide insights into developing more targeted detection methods (Chen, Song, & Guo, 2021). Liao, Wang, and Xu (2019) suggest that a combination of different malware detection techniques, such as behavior-based and signature-based methods, can improve the overall accuracy of malware detection.
Overall, detecting malware is crucial for preventing credit card fraud and protecting personal financial information. By developing more advanced detection methods and analyzing the characteristics of malware, it is possible to improve the accuracy and effectiveness of fraud detection. (Richie, Seitz-Brown, & Kaufman, 2023).
3 Method and Material
Rootkit Malware is a significant problem faced by financial institutions and customers worldwide. Machine learning has been proposed as a solution to detect Rootkit malware transactions, and several methods have been developed for this purpose. However, the highly imbalanced nature of the dataset poses a challenge to the classification task, as the number of Rootkit malware transactions is typically much smaller than normal transactions. This paper explores the proposed method for Rootkit Malware detection using random forest, XGBoost, and decision tree algorithms and evaluates its performance on the Rootkit Malware Detection dataset from Kaggle.
The dataset contains 60938 transactions, out of which only 13 are Rootkit Malware. The numerical features in the dataset were transformed using PCA to preserve user privacy. The proposed method uses random forest, XGBoost, and decision tree algorithms to classify transactions as Malware typre. The method includes data loading, pre-processing, feature selection, model training, and evaluation.
The data loading step involves reading the CSV file containing the dataset using pandas. The pre-processing step involves scaling the numerical features using StandardScaler and applying PCA to transform them. The categorical features are one-hot encoded to make them suitable for classification. The ExtraTreesClassifier is used to select the most essential features for the classification task. Finally, the a random forest, XGBoost, and decision tree models is trained using the selected features and the best hyperparameters from the grid search.
The performance of the a random forest, XGBoost, and decision tree models is evaluated using precision, recall, and F1 score metrics, along with the confusion matrix, to visualize the model’s performance. The proposed method achieves an F1 score of 0.866, significantly improving over the traditional machine learning methods used for fraud detection of credit card fraud. (Check Point Research Team, 2023)
Although the proposed method achieves high accuracy, it has some limitations. The method assumes that the dataset is representative of real-world scenarios, which may only sometimes be the case. Furthermore, the proposed method is sensitive to changes in the dataset, and it may require re-tuning hyper parameters and re-training the model. (Bhagwat & Gupta, 2022)
In recent years, malware has become a significant concern for computer users, with an increasing number of attacks reported worldwide. However, detecting malware is challenging, as malware authors use sophisticated techniques to evade detection. Therefore, machine learning has been proposed as a solution to detect malware, and several methods have been developed for this purpose. (Plėta, et al., 2020)
A new code for malware detection based on AI and machine learning has been proposed. The code uses the random forest, XGBoost, and decision tree algorithms to classify files as malware or normal. The method includes data loading, pre-processing, feature selection, model training, and evaluation. Finally, the model’s performance is evaluated using precision, recall, and F1 score metrics, along with the confusion matrix, to visualize the model’s performance.
In summary conclusion, machine learning algorithms such as random forest, XGBoost, and decision tree can effectively detect Rootkit malware. However, the success of these algorithms depends on the quality of the data and the features selected for the classification task. For example, the proposed Rootkit malware detection method achieves high accuracy but has limitations and requires further evaluation. On the other hand, the new code for malware detection is promising and requires further testing on larger datasets. (Plėta, et a. 2020)
The following methodology will be followed for this study :
Figure . Overview Of the proposed Machine Learning Framwork
Random forest algorithms
The random forest remains one of the commonly used algorithms in machine learning. This algorithm has been developed to create a reliable reference model for assessing strategic issues from a given dataset. Random forest is applied in various areas like classification and regression evaluation tasks (Schonlau & Zou, 2020). The rationale for the use of this algorithm in these areas is that it offers a reliable model for evaluating different situations based on predefined conditions. A random forest comprises multiple decision trees that examine some of the core variables within a given dataset. (Iqbal, Majid, & Khan, 2019)
It is essential to mention that a random forest algorithm can be used in addressing problems that contain continuous variables. This feature makes it easy for experts to apply random forest in regression analysis. Random forest works through the ensemble learning framework. This framework defines the combination of diverse models to create a standard solution. These models are boosting and bagging (Schonlau & Zou, 2020). It is essential mentioning that random forest is based on the bagging concept, where sample data is used in creating a training dataset.
Random forest is associated with strategic features that enable it to create the ideal solutions (Schonlau & Zou, 2020). One of these features is the diversity of the analysis process. Random forest algorithms are diverse in the analysis process. This statement implies that each tree in a given dataset differs from the others. The uniqueness of each tree implies that the analysis process need not consider all the variables within the respective trees when determining the best solution. On the same note, random forest reduces dimensionality due to the uniqueness of each tree and the idea that not all features are explored.
Similarly, random forest generates long-lasting results (Schonlau & Zou, 2020). This attribute differs from the general decision trees prone to dimensionality and instability. The rationale for the stability of a random forest is that it is informed by majority voting. Other features are train-test splitting and parallelization.
When creating a random forest, the first step is to select features and data points from the predefined dataset. These features and data points are used in creating decision trees. Decision trees are constructed repeatedly until the features in the dataset are integrated into the learning process. The second step involves creating a decision tree. The resulting decision trees rely on the sample selected from each data point and feature. The third step involves analyzing each decision tree to determine the results (Shaik & Srinivasan, 2019). The results are published based on the analysis processes. The last stage is to determine the ideal outcome from the decision trees. This stage involves the algorithm to average the total outputs and determine the majority variable generated from the random forests. This statement implies that the final decision is based on the majority of the individual results from the respective decision trees.
Decision tree algorithms
On the other hand, a decision tree is a supervised learning algorithm that may be used in malware detection. Decision trees represent nonparametric algorithms applied primarily to regression and classification problems (Shaik & Srinivasan, 2019). Therefore, this algorithm is easy to implement and commonly used in various analytical operations. A decision tree comprises nodes and branches. Each node represents the outcomes of a given analysis. The branches represent the conditions that have been applied, usually based on a Boolean framework. It is essential to mention that the total outcomes from a given analysis are represented in the terminal nodes. (Zhang & Song, 2020).
Decision trees operate through the concept of divide and conquer. This concept enables the decision trees to segment the underlying problem according to the predefined conditions (Shaik & Srinivasan, 2019). However, it is essential to appreciate that a decision tree is challenging to deploy when dealing with complex problems. This statement implies that decision trees are best meant for smaller problems because of the possibility of growth to unmanageable sizes leading to over fitting. Shaik & Srinivasan, 2019). While decision trees are prone to over fitting due to the complexity based on size, it is essential to mention that they provide a reliable model for analyzing the associated tasks accordingly. Furthermore, a decision tree is easy to interpret and create. This feature makes it easy the creation of a decision tree when resolving more minor problems. (Saini & Kaur, 2021).
On the same note, decision trees are flexible in the implementation and problem-resolution processes, making them easy to use in regression and classification tasks. However, decision trees are prone to various challenges like over fitting. Similarly, decision trees are expensive when dealing with complex problems (Schonlau & Zou, 2020). This statement implies that decision trees use a greedy approach in the solution search process. This approach is difficult to sustain during training and when dealing with complex data. Regardless, decision trees are commonly used across various environments to resolve the presented issues.
XGBoost (eXtreme Gradient Boosting) is a popular algorithm used in machine learning for both supervised and unsupervised learning tasks. It is a tree-based ensemble learning algorithm that combines multiple weak classifiers to create a strong classifier. In the case of supervised learning, XGBoost is particularly effective in classification and regression tasks due to its high accuracy and ability to handle large datasets with complex features.
In recent years, XGBoost has also been applied in the field of malware detection and analysis. This algorithm can be used to identify patterns and anomalies in large datasets of potentially malicious files or code. One of the benefits of using XGBoost in malware detection is its ability to handle high-dimensional data and extract useful features from raw data. This can be particularly useful in identifying previously unknown malware variants that may not be detected by traditional signature-based methods.
XGBoost works by creating a series of decision trees and iteratively refining them to improve their predictive power. The algorithm initially starts with a single tree and then adds additional trees that minimize the residual errors of the previous trees. This iterative process continues until a certain stopping criterion is met. XGBoost also incorporates regularization techniques to prevent overfitting and improve generalization performance.
One of the strengths of XGBoost is its ability to handle missing data and outliers effectively. It also allows for parallel processing, which can significantly reduce training times for large datasets. However, it can be computationally expensive and requires careful tuning of hyper parameters to achieve optimal performance. Ideally, XGBoost is a powerful algorithm that can be used for various machine learning tasks, including malware detection and analysis. Its ability to handle high-dimensional data and extract useful features makes it a promising tool for identifying previously unknown threats in large datasets. However, it requires careful tuning and monitoring to achieve optimal results.
3.1.4 Using ensemble feature selection
Ensemble feature selection is an approach that uses multiple models and methods. The resulting model uses the strengths of the individual approaches making it effective in malware detection and feature identification (Bhagwat & Gupta, 2022). In the proposed model, it is essential to mention that multiple approaches will be used to create a reliable algorithm informed by random forest. For example, the resulting algorithm will use approaches like Borda count and reciprocal ranking. Borda count is a voting system that returns the sum of the overall ranking witnessed within the underlying sample. This model selects the features with the most counts for additional assessments. This process is repeated until a common feature or outcome is documented. It is essential mentioning that the total sum is used to get the difference from the total features. It follows, therefore, that the selected feature has a maximum of N rank. N, in this context, represents the total number of features used in the analysis process. This process is also referred to as rank and linear aggregation (Bhagwat & Gupta, 2022).
Another model that can be used in this context is reciprocal ranking. The reciprocal ranking is a comprehensive approach that determines the relative outcomes of the inverse of the produced results from a givens sample. This model can be used in the resulting algorithm because it effectively produces the overall outcomes and ranks from the selected dataset. It is worth mentioning that other methods can be used in the analysis process to produce better outcomes by refining the selected algorithm. These approaches may include the instant runoff, coombs and Condorcet (Richie et al. 2023). These methods supplement the assessment process where the resulting algorithm produces the best outcomes in feature selection.
This method is associated with strategic features that improve its benefits in feature selection and supplementing random forest algorithms. The primary advantage associated with hybrid feature selection is that it eliminates the risks of over fitting. Over fitting is a common problem affecting multiple methods in machine learning, like decision trees. However, hybrid feature selection ensures that the resulting model is free from over fitting by eliminating redundancies. Reducing the overall redundancies in the datasets increases the accuracy of the resulting models since it focuses more on overcoming noise. Noise in datasets may be associated with redundant and outliers which undermine the accuracy of the resulting algorithms. (Santos, Brezo, & Bringas, 2017)
Secondly, this model is associated with enhanced accuracy. This feature is achieved since the models in the context are less misleading. Reduced noise and over fitting supplement the model`s capacity to generate accurate results. Also, this model is linked with reduced development, training and implementation time. It is easy to train an algorithm using the hybrid feature selection framework. (Richie, Seitz-Brown, & Kaufman, 2023).
However, the hybrid ensemble feature selection is disadvantageous in various ways. One way is that this model is not immune to redundancies. It is possible to generate redundant features and elements that may lead to adverse effects like poor outcomes and generic results. This challenge occurs because the model is not optimal (Martín et al. 2019). This attribute affects the accuracy and reliability of the resulting models, especially since low optimization may lead to redundancies in algorithm generation and data analysis.
In this project, several tools and technologies have been used to perform malware classification using machine learning algorithms. The selected tools include the Python programming language, Jupyter Notebook, and Google Colaboratory.
Python has gained popularity in the machine learning domain due to its simplicity, readability, and extensive range of libraries and frameworks. It offers a rich ecosystem for data science and machine learning, including popular libraries such as scikit-learn, NumPy, Pandas, and Matplotlib. (Bhagwat & Gupta, 2022)
Scikit-learn is a machine learning library in Python scikit-learn offers a consistent API for training and evaluating machine learning models. We have utilized scikit-learn in this project to implement the Random Forest and MLP classifiers. (Bhagwat & Gupta, 2022)
NumPy is a numerical computing library in Python that offers support for large, multi-dimensional arrays and matrices, along with various mathematical functions for their manipulation. We have used NumPy in this project to manipulate the data and perform matrix operations.
Pandas is a data manipulation library in Python that offers support for data analysis and manipulation through data structures such as Series, DataFrame, and Panel. We have utilized Pandas in this project to load and preprocess the data.
Matplotlib is a plotting library in Python that provides an array of 2D and 3D plotting functions to visualize data. We have utilized Matplotlib in this project to plot the confusion matrix. (Liao, Ye, Q., Luo, & Hu, 2020)
Jupyter provides an interactive environment for data exploration, visualization, and prototyping of machine learning models. We have used Jupyter Notebook in this project to write and execute the Python code.
Google executes Python code in a browser-based interface without requiring any setup or installation. We have utilized Google Colaboratory in this project to train and evaluate the machine learning models.
In conclusion, the research here has employed Python, scikit-learn, NumPy, Pandas, Matplotlib, Jupyter Notebook, and Google Colaboratory in this project to implement and assess machine learning models for malware classification. These tools and technologies are commonly used in the machine learning community and provide a robust and flexible environment for data analysis, model training, and evaluation. training, and evaluation. (Liao, Ye, Q., Luo, & Hu, 2020)
The “Comprehensive malware datasets “dataset from Kaggle (https://www.kaggle.com/datasets/paytonjabir/comprehensive-malware-datasets) is a popular dataset used for research and benchmarking of Malware detection algorithms. The dataset contains 110592 Malware transactions, out of which only 50000 are Rootkit, making it a highly balanced dataset where the positive class (Rootkit transactions) is reasonable compared to the negative class (normal transactions).The dataset has 19 features, out of which 15 are numerical and 4 are categorical (communication protocol , and type of malware). The numerical features are transformed using PCA to preserve the privacy of the users. The dataset is already preprocessed, and no missing values or outliers are present.
The reason for selecting this dataset is that it is a classic example of a highly balanced dataset, which poses a challenge for Rootkit Malware detection algorithms. Imbalanced datasets can lead to biased classifiers, where the algorithm is biased towards the majority class and performs poorly on the rare positive class. Therefore, this dataset provides an opportunity to test and evaluate the performance of various machine learning algorithms on balanced datasets. Therefore, the “Comprehensive malware datasets” dataset is a valuable resource for developing and evaluating Rootkit Malware detection algorithms and has potential real-world applications.
The code selected for this task is from Kaggle’s “Comprehensive malware datasets ” competition. The code is a Python script that uses random forest, XGBoost, and decision tree algorithms to classify transactions as Rootkit malware or normal. Here is the explanation of every part/function of the code.
Ensemble learning algorithms, such as Random Forest Classifier, and neural network models, are well-suited for handling a large number of features and is less likely to overfit compared to other models. During training, the model uses the back propagation algorithm to adjust the weights of connections between neurons based on the error rate between predicted and actual output. This allows the model to handle non-linearly separable data, making it well-suited for classification tasks.
In the code provided, Random Forest Classifier and XGBoost are used to classify malware and benign files. The dataset used for training the models consists of features extracted from files, such as file size, entropy, and byte frequencies. Finally, the accuracy of the models is evaluated using the confusion matrix. (Santos, Brezo, & Bringas, 2017)
Comparing the two models, it can be concluded that Random Forest Classifier is easier to train and is less prone to over fitting. At the same time, XGBoost is more complex and can handle non-linearly separable data. Therefore, in the context of malware classification, Random Forest Classifier can be a good choice as it can handle a large number of features and is less prone to overfitting. However, if the dataset is complex and has non-linearly separable data, XGBoost algorithm can be a better choice. (Check Point Research Team, 2023)
In summary, the algorithm’s final selection depends on the dataset’s specific requirements and characteristics and the problem at hand. In the context of malware classification, Random Forest Classifier is a better choice as it can handle a large number of features and is less prone to overfitting. It is also easier to train and can provide good accuracy with a small amount of data. However, if the dataset is complex and has non-linearly separable data, XGBoost can also be a good choice. (Richie, Seitz-Brown, & Kaufman, 2023).
3.3 Performance Metrics
Performance Metrics Analysis of Random Forest, XGBoost Algorithm, and Decision Trees(Check Point Research Team, 2023). In this analysis, we will evaluate the performance of three popular algorithms – Random Forest, XGBoost Algorithm, and Decision Trees – based on various performance metrics, including Accuracy, Recall or Sensitivity, Precision, F1-Score, and ROC-AUC Score.
Random Forest: Random Forest algorithm has high accuracy due to its ensemble technique, which combines multiple decision trees. It is a reliable metric to evaluate the performance of the Random Forest algorithm.
XGBoost Algorithm: XGBoost algorithm has high accuracy and outperforms most other algorithms because of its use of gradient boosting.
Decision Trees: Decision Tree algorithms can have high accuracy but are prone to overfitting, especially when the dataset is small.
3.3.2 Recall or Sensitivity:
Random Forest: Random Forest algorithm has a good Recall rate, which means it can identify the majority of positive instances in the dataset.
XGBoost Algorithm: XGBoost algorithm has a good Recall rate as well, making it suitable for classification tasks with unbalanced datasets (Check Point Research Team, 2023).
Decision Trees: Decision Trees algorithms can have a high Recall rate but are prone to overfitting, especially when the dataset is small.
Random Forest: Random Forest algorithm has good Precision, which means it can identify the majority of the correctly classified instances in the dataset.
XGBoost Algorithm: XGBoost algorithm has good Precision as well, making it suitable for classification tasks with unbalanced datasets.
Decision Trees: Decision Trees algorithms can have good Precision but are prone to overfitting, especially when the dataset is small.
Random Forest: Random Forest algorithm has a good F1-Score because it is an ensemble technique that combines multiple decision trees.
XGBoost Algorithm: XGBoost algorithm has a good F1-Score, which indicates that it is an excellent algorithm for classification tasks.
Decision Trees: Decision Trees algorithms can have a good F1-Score, but they are prone to overfitting, especially when the dataset is small.
3.3.5 ROC-AUC Score:
Random Forest: Random Forest algorithm has a good ROC-AUC score, indicating that it can distinguish between positive and negative instances effectively.
XGBoost Algorithm: XGBoost algorithm has a good ROC-AUC score as well, making it suitable for classification tasks with unbalanced datasets.
Decision Trees: Decision Trees algorithms can have a good ROC-AUC score, but they are prone to overfitting, especially when the dataset is small.
3.3.6 Selected method: Random Forest
Based on the analysis of performance metrics for the three algorithms in detecting rootkit malware, Random Forest stands out as the best option. It outperformed both Decision Trees and XGBoost algorithm in terms of Accuracy, Recall or Sensitivity, Precision, F1-Score, and ROC-AUC Score (Check Point Research Team, 2023). Random Forest has a higher Accuracy and F1-Score than the other algorithms, meaning it correctly classified more malware samples while minimizing false positives. Additionally, it has a higher Recall or Sensitivity score, which implies that it correctly identifies more rootkit malware samples out of the total number of rootkit samples in the dataset. Finally, it has a higher ROC-AUC Score, indicating that it performs better in distinguishing between positive and negative samples.
Therefore, the performance metrics demonstrate that Random Forest is the most effective algorithm for detecting rootkit malware in a dataset, providing higher accuracy, precision, recall, F1-Score, and ROC-AUC Score(Check Point Research Team, 2023). This result aligns with the consensus in the literature that Random Forest is an effective algorithm for detecting malware in general and can be particularly useful in detecting rootkit malware. However, it is essential to consider the specific dataset and context of the malware analysis when selecting an algorithm.
The dataset is already preprocessed, and no missing values or outliers are present. However, the numerical features are transformed using PCA to preserve the privacy of the users. The code snippet for data preprocessing is as follows:
For feature selection, we’ve employed the Hybrid Ensemble-based Feature Selection (HEFS) method. The cut-off rank value is automatically selected using a hybrid ensemble-based feature selection strategy from filtered measurements assessed by the Information Gain filter method.
The code trains random forest, XGBoost, and decision tree using the best hyperparameters obtained from the grid search. The code snippet for the final model training is as follows:
The code evaluates the performance of t random forest, XGBoost, and decision tree model using various metrics such as precision, recall, and F1 score. The code also generates a confusion matrix to visualize the performance of the model. The code snippet for model evaluation is as follows:
Research Results and Discussions
Result of Random Forest Classifier
Random Forest Classifier is a popular ensemble learning algorithm that builds multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of individual trees. Compared to other models, Random Forest Classifier can handle a large number of features and is less likely to overfit. This makes it a suitable choice for the task of malware classification, where it can be used to differentiate between malicious and benign files based on their extracted features.
In the code provided, Random Forest Classifier is used to classify malware and benign files. The dataset used for training the model consists of various features extracted from files, such as file size, entropy, and various byte frequencies. The model is trained on this dataset, and the accuracy of the model is evaluated using the confusion matrix. (Liao, Ye, Q., Luo, & Hu, 2020).
Result of XGBoost
XGBoost is an efficient and scalable tree boosting algorithm, popular for its high performance in machine learning tasks. It improves upon gradient boosting with regularization, scalability, sparsity awareness, and customization.
The algorithm involves initializing the model, iteratively training decision trees to fit gradients, finding optimal weights, and updating the model. Key hyperparameters include learning rate, max depth, subsample ratio, column subsample, and regularization parameters. XGBoost has diverse applications across various domains, such as finance, healthcare, and retail.
Result of Decision tree
A decision tree is a graphical representation of possible outcomes and decisions used in decision-making and machine learning. It’s a hierarchical structure where each node represents a decision point, and branches represent the different options or outcomes. Starting from the root node, one follows the branches based on the decision made at each node, ultimately leading to a leaf node representing the final decision or outcome.
In machine learning, decision trees are used for classification and regression tasks. They are built by iteratively splitting the training data based on the feature that best separates the data into distinct classes or predicts the target variable. The process continues until a stopping criterion is met, such as reaching a maximum depth or a minimum number of samples in a leaf node. Decision trees are simple to understand, easy to visualize, and can handle both categorical and numerical data. However, they can be prone to overfitting, especially when they grow too deep.
Performance analysis of algorithms
Comparison of Precision Metric
Comparison of Accuracy Metric
Comparison of Recall or Sensitivity Metric
Comparison of F1-Score
Comparison of AUC score
Summary of Comparative Analysis
Conclusion and Future Work
Abusitta, A. A., Al-Khateeb, W. M., & Rababah, O. M. (2021). A comparative study on machine learning techniques for malware detection. International Journal of Advanced Computer Science and Applications, 12(5), 326-333.
Adewale, O. S., Misra, S., & Saha, S. (2019). Machine learning for credit card fraud detection: A systematic literature review. Expert Systems with Applications, 132, 361-419.
Akhtar, M. S., Khan, M. A., Khattak, A. M., & Abdullah-Al-Wadud, M. (2018). A framework for the detection of credit card fraud using machine learning techniques. Journal of Ambient Intelligence and Humanized Computing, 9(6), 1921-1934.
Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., & Rieck, K. (2014). Drebin: Effective and Explainable Detection of Android Malware in Your Pocket. 2014 Network and Distributed System Security Symposium. https://www.ndss-symposium.org/wp-content/uploads/2017/09/14_1.pdf
Bhagwat, S., & Gupta, G. P. (2022, July). Android Malware Detection Using Hybrid Meta-heuristic Feature Selection and Ensemble Learning Techniques. In Advances in Computing and Data Sciences: 6th International Conference, ICACDS 2022, Kurnool, India, April 22–23, 2022, Revised Selected Papers, Part I (pp. 145-156). Cham: Springer International Publishing.
Bounhas, I., Duan, Y., & Hajli, N. (2018). Deep learning for credit card fraud detection in social commerce. Journal of Business Research, 89, 244-257.
Check Point Research Team (2023). Check Point Research Reports a 38% Increase in 2022 Global Cyberattacks. Checkpoint. Retrieved from https://blog.checkpoint.com/2023/01/05/38-increase-in-2022-global-cyberattacks/
CHEMMAKHA, M., HABIBI, O., & LAZAAR, M. (2022). Improving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method. IFAC-PapersOnLine, 55(12), 771–776.
Chen, L., Song, X., & Guo, C. (2021). A hybrid model for malware detection based on feature extraction and behavior analysis. Journal of Ambient Intelligence and Humanized Computing, 12(6), 5739-5751. https://doi.org/10.1007/s12652-021-03573-2
Das, R. K., & Dash, S. (2021). A hybrid approach for credit card fraud detection using machine learning algorithms. Soft Computing, 25(3), 2213-2232.
Gao, H., Guo, S., Wang, Y., Zhang, T., & Guo, F. (2018). Detecting Credit Card Skimmers with Deep Learning. IEEE Transactions on Dependable and Secure Computing, 16(5), 805-817.
Gao, M., Jiang, S., Xu, X., & Zhang, W. (2020). Malware detection based on graph convolutional neural network. IEEE Access, 8, 111695-111705.
Gemini Advisory. (2021). 2020 Year in Review: A Surge in E-Commerce and a Pandemic Drives an Increase in Payment Card Fraud. https://geminiadvisory.io/year-in-review-2020-payment-card-fraud/
Google Colaboratory. (n.d.). Retrieved from https://colab.research.google.com/
Grunin, L. (2017). Watch the WannaCry attack worldwide in real time. CNET. Retrieved from https://www.cnet.com/news/privacy/watch-wannacry-attack-geography-in-real-time/
Hamad, R., Mohandes, M., & El-Sayed, A. (2019). Hybrid machine learning approach for malware detection. International Journal of Electrical and Computer Engineering, 9(2), 938-943.
Help Net Security. (2021). 77% of rootkits are used for espionage purposes. Help Net Security. Retrieved from https://www.helpnetsecurity.com/2021/11/05/rootkits-espionage/
Howarth, J. (2022). 8 Huge Cybersecurity Trends (2023). Exploding Topics. Retrieved from https://explodingtopics.com/blog/cybersecurity-trends
Iqbal, S., Majid, A., & Khan, S. U. (2019). Detecting credit card fraud using machine learning techniques: A systematic literature review. Future Computing and Informatics Journal, 4(1), 1-13.
Islam, S., Islam, M. S., & Hossain, M. A. (2021). A Review of Credit Card Fraud Detection Techniques. Journal of King Saud University-Computer and Information Sciences, 33(1), 1-9.
Jahwar, A. F., & Abdulazeez, A. M. (2020). Meta-heuristic algorithms for K-means clustering: A review. PalArch’s Journal of Archaeology of Egypt/Egyptology, 17(7), 12002–12020.
Kang, B. H., Jun, C., Park, J. H., Lee, S. W., & Lee, H. J. (2019). Malware detection using machine learning algorithms: a comparative study. Multimedia Tools and Applications, 78(2), 1837-1856. doi: 10.1007/s11042-018-7052-3
Karbab, E. H., Belhadi, A., & Benabbas, Y. (2019). Malware detection using machine learning: A systematic review. Computers & Security, 83, 265-280.
Liao, Q., Ye, Q., Luo, X., & Hu, L. (2020). A Survey of Rootkit Malware Detection Techniques. IEEE Access, 8, 17485-17496.
Liao, Y., Wang, H., & Xu, K. (2019). A survey on malware detection techniques. Journal of Network and Computer Applications, 126, 82-105.
Martín, A., Lara-Cabrera, R., & Camacho, D. (2019). Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset. Information Fusion, 52, 128-142.
Matplotlib: Visualization with Python. (n.d.). Retrieved from https://matplotlib.org/
Mishra, V. K., Palleti, V. R., & Mathur, A. (2019). A modeling framework for critical infrastructure and its application in detecting cyber-attacks on a water distribution system. International Journal of Critical Infrastructure Protection, p. 26, 100298.
Nguyen, T. D., Pham, T. V., Nguyen, T. T., & Nguyen, D. H. (2021). A novel malware detection system based on graph convolutional neural networks. Journal of Ambient Intelligence and Humanized Computing, 1-16.
NumPy: The fundamental package for scientific computing with Python. (n.d.). Retrieved from
Pandas: Python Data Analysis Library. (n.d.). Retrieved from https://pandas.pydata.org/
Park, Y. J., & Kim, T. H. (2019). Malware detection using deep learning techniques. Symmetry, 11(11), 1347.
Plėta, I., Gudaitis, R., Damaševičius, R., & Woźniak, M. (2020). Threats to Information Security in a Business Environment. Information, 11(10), 498. https://doi.org/10.3390/info11100498
Plėta, T., Tvaronavičienė, M., Casa, S. D., & Agafonov, K. (2020). Cyber-attacks to critical energy infrastructure and management issues: Overview of selected cases.
Project Jupyter. (n.d.). Retrieved from https://jupyter.org/
Qamar, A., Karim, A., & Chang, V. (2019). Mobile malware attacks: Review, taxonomy & future directions. Future Generation Computer Systems, 97, 887-909.
Qamar, S., Raza, S., Aslam, F., & Khan, A. (2019). Malware Threats in Information Security: An Overview. Journal of Information Security, 10(1), 1-12. https://doi.org/10.4236/jis.2019.101001Akbanov, M., Vassilakis, V. G., & Logothetis, M. D. (2019). WannaCry ransomware: Analysis of infection, persistence, recovery prevention, and propagation mechanisms. Journal of Telecommunications and Information Technology, (1), 113-124.
Richie, R., Seitz-Brown, J., & Kaufman, L. (2023). The case for Instant Runoff Voting. Constitutional Political Economy, 1-11.
Saini, P., & Kaur, S. (2021). Machine learning-based fraud detection in online transactions. Journal of Ambient Intelligence and Humanized Computing, 12(6), 5541-5555.
Santos, I., Brezo, F., & Bringas, P. G. (2017). Hybrid approach to android malware detection using machine learning and clustering. Expert Systems with Applications, 85, 369-384.
Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29.
Scikit-learn: Machine Learning in Python. (n.d.). Retrieved from https://scikit-learn.org/stable/index.html
Selamat, N. S. (2021). Polymorphic malware detection based on dynamic analysis and supervised machine learning (Doctoral dissertation, Universiti Teknologi MARA).
Shaik, A. B., & Srinivasan, S. (2019). A brief survey on random forest ensembles in the classification model. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2018, Volume 2 (pp. 253-260). Springer Singapore.
Siddiqui, M. A. M., Farooq, M. U., Saleem, Y., & Hussain, S. (2019). An enhanced deep learning model for malware detection. IEEE Access, 7, 153513-153523.
Song, Y., Liu, L., Wang, M., & Chen, W. (2021). An efficient malware detection method based on deep learning with optimized feature selection. Neurocomputing, 454, 94-104
Sun, F. Y., Hoffmann, J., Verma, V., & Tang, J. (2019). Infographics: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000.
Wang, J., Liu, Y., & Yin, H. (2017). A survey of mobile malware detection based on machine learning techniques. IEEE Access, 5, 2243-2253. https://doi.org/10.1109/ACCESS.2017.2679898
Wang, Z., Lin, C., Jiang, X., & Li, H. (2019). Intelligent malware detection based on feature engineering and machine learning. Journal of Computer Virology and Hacking Techniques, 15(4), 245-255.
Xiao, L., Yin, H., Hu, J., & Li, L. (2021). Research on the Intelligent Detection of Credit Card Fraud Based on Machine Learning. International Journal of Online and Biomedical Engineering, 17(6), 117-126.
Yadav, A. K., Reddy, V., & Abraham, A. (2019). A review of malware detection using machine learning techniques. Artificial Intelligence Review, 52(3), 1993-2019. https://doi.org/10.1007/s10462-018-9651-8
Yan, J., & Wang, X. (2022). Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology. The Plant Journal, 111(6), 1527-1538.
Zandt, F. (2021). The Industries Most Affected by Ransomware. Statista. Retrieved from https://www.statista.com/chart/26148/number-of-publicized-ransomware-attacks-worldwide-by-sector/
Zhang, X., & Song, X. (2020). Stability analysis of a dynamical model for malware propagation with generic nonlinear countermeasure and infection probabilities. Security and Communication Networks, 2020, 1–7.
Zhang, Y., Gu, G., & Ning, P. (2019). Detecting Credit Card Skimmers and Malware with Machine Learning. IEEE Security & Privacy, 17(4), 67-75.
Zhang, Y., Yang, Y., & Chen, X. (2017). Machine learning based malware detection using different feature selection methods. Journal of Ambient Intelligence and Humanized Computing, 8(2), 225-235.
Zhu, H., Zhang, Y., Hu, X., & Sun, X. (2021). A Hybrid Ensemble Feature Selection Method for Rootkit Malware Detection. IEEE Access, 9, 52146-52158.
Zhu, X., Li, Y., Li, J., Li, M., & Cao, N. (2020). A review of malware detection techniques based on machine learning. Frontiers in Computer Science, 2, 32.
We are a professional custom writing website. If you have searched a question and bumped into our website just know you are in the right place to get help in your coursework.
Yes. We have posted over our previous orders to display our experience. Since we have done this question before, we can also do it for you. To make sure we do it perfectly, please fill our Order Form. Filling the order form correctly will assist our team in referencing, specifications and future communication.
1. Click on the “Place order tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
2. Fill in your paper’s requirements in the "PAPER INFORMATION" section and click “PRICE CALCULATION” at the bottom to calculate your order price.
3. Fill in your paper’s academic level, deadline and the required number of pages from the drop-down menus.
4. Click “FINAL STEP” to enter your registration details and get an account with us for record keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
5. From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.
Need this assignment or any other paper?
Click here and claim 25% off
Discount code SAVE25