Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity

• URL: http://arxiv.org/abs/2302.12415v3
• Date: Sat, 2 Mar 2024 10:47:19 GMT
• Title: Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity
• Authors: Khatoon Mohammed,
• Abstract summary: Traditional signature-based methods of malware detection have limitations in detecting complex threats. In recent years, machine learning has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are difficult for humans to identify.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As cyber attacks continue to increase in frequency and sophistication, detecting malware has become a critical task for maintaining the security of computer systems. Traditional signature-based methods of malware detection have limitations in detecting complex and evolving threats. In recent years, machine learning (ML) has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are 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 the integration of multiple ML algorithms and the use of explainable AI techniques to enhance the interpret ability of ML-based detection systems. Our research highlights the potential of ML-based techniques to improve the speed and accuracy of malware detection, and contribute to enhancing cybersecurity

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