  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
