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

翻译：随着网络攻击的频率和复杂性持续增加，检测恶意软件已成为维护计算机系统安全的关键任务。传统的基于签名检测方法在应对复杂且不断演变的威胁时存在局限性。近年来，机器学习（ML）作为有效检测恶意软件的前景性解决方案应运而生。ML算法能够分析海量数据集，识别人类难以发现的模式。本文全面综述了恶意软件检测中前沿的ML技术，包括监督学习、无监督学习、深度学习和强化学习。同时，我们考察了基于ML的恶意软件检测面临的挑战与局限，如对抗性攻击的潜在风险及对大量标注数据的需求。此外，我们探讨了基于ML的恶意软件检测未来发展方向，包括多ML算法的集成以及利用可解释AI技术增强ML检测系统的可解释性。研究凸显了基于ML的技术在提升恶意软件检测速度与准确性方面的潜力，及其对强化网络安全的贡献。