# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
###### Abstract  
Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to detect zero-day attacks, polymorphic malware, increasing signature base, and detection speed. To achieve rapid detection, automated system-level malware detection using machine learning approaches, leveraging the power of artificial intelligence to identify and mitigate malware attacks, has emerged as a promising solution. This comprehensive review aims to provides a detailed analysis of the status quo in malware detection by exploring the fundamentals of machine learning techniques for malware detection. The review is largely based on the PRISMA approach for article search methods and selection from four databases. Keywords were identified together with inclusion and exclusion criteria. The review seeks feature extraction and selection methods that enhance the accuracy and precision of detection algorithms. Evaluation metrics and common datasets were used to assess the performance of the system-level malware detection techniques. A comparative analysis of different machine learning approaches, emphasizing their strengths, weaknesses, and performance in detecting system-level malware is presented together with the limitations of the detection techniques. The paper concludes with future research opportunities, particularly in applying artificial intelligence, and provides a resource for researchers and cybersecurity professionals seeking to understand and advance automated system-level malware detection using machine learning. a _applied_  
_sciences_  
literature review of research publications based on the many strategies, algorithms, and procedures used in system-level malware detection. The review incorporates several aspects of automated system-level malware detection. This is distributed across several machine learning techniques, specifically, Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks, Decision Tree, Random Forrest, Fuzzy Logic, Genetic Algorithm, General Adversarial Network, Hidden Markov Model, and Swarm Intelligence. The study investigates feature extraction and selection methods that enhance the reliability of malware detection algorithms. A comprehensive analysis of the strengths, limitations, and performance of different machine learning algorithms is conducted and these are identified as areas for improvement and future research opportunities. The review discusses commonly used evaluation metrics to assess the performance of these algorithms and the datasets utilized for evaluating system-level malware detection. The challenges and limitations associated with automated system-level malware detection using machine learning, such as dataset availability, label imbalance, adversarial attacks, and generalization of models are addressed. By critically examining these challenges, we aim to highlight areas for improvement and future research directions. Additionally, we conduct a comparative analysis of different machine learning approaches, enabling us to evaluate their respective strengths and weaknesses in detecting malware at the system level. The findings of this study will contribute to the advancement of malware detection and provide guidance for future research efforts for the development of more robust and reliable malware detection systems. The features of this research work are as follows:  
1. This work puts forward research work of various feature extraction and classification strategies for malware detection in recent years (2003-2022). 2. It helps cyber specialists in integrating machine learning technology in detecting various anomalies in the automated system level. 3. The effectiveness and morality of ongoing plans are shown through quantitative analysis using numerous measurements. 4.