Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity
Rebet Keith Jones, Marwan Omar, Derek Mohammed, Calvin Nobles, Maurice Dawson
Abstract
As cyberattacks continue to increase in frequency and sophistication, detecting malware has become a critical task for maintaining the security of computer systems. Traditional signature-based malware detection methods have limitations in detecting complex and evolving threats. Machine learning (ML) has emerged as a promising solution to detect malware effectively in recent years. ML algorithms can analyze large datasets and identify patterns difficult for humans to identify. This paper presents a comprehensive review of the state-of-the-art ML techniques used in malware detection, including supervised and unsupervised learning, deep learning, and reinforcement learning. We also examine the challenges and limitations of ML- based malware detection, such as the potential for adversarial attacks and the need for large amounts of labeled data. Furthermore, we discuss future directions in ML-based malware detection, including integrating multiple ML algorithms and using explainable AI techniques to enhance the interpretability of ML-based detection systems. Our research highlights the potential of ML-based techniques to improve the speed and accuracy of malware detection and enhance cybersecurity.