综述机器学习与深度学习在网络安全与数字取证中的应用与挑战
Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review
- 系统梳理了多种AI技术在入侵检测与恶意软件分类中的应用
- 指出现有方法在透明性与可扩展性上的不足
- 适合关注安全AI落地的科研人员与从业者参考
在快速发展的网络安全与数字取证领域，机器学习（ML）和深度学习（DL）已成为变革性技术，提供了识别、阻止和分析网络风险的新方法。本文综述了这些领域中使用的ML与DL方法，展示其优势、局限与潜力。涵盖用于检测系统入侵、分类恶意软件以预防攻击、发现异常并增强系统韧性的多种人工智能技术。研究最后指出了亟待深入探索的方向，并提出构建透明、可扩展的ML/DL解决方案的建议，以适应不断演变的网络安全与数字取证环境。
原文摘要 · Abstract (English)
In the paced realms of cybersecurity and digital forensics machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL approaches used in these fields showcasing their advantages drawbacks and possibilities. It covers a range of AI techniques used in spotting intrusions in systems and classifying malware to prevent cybersecurity attacks, detect anomalies and enhance resilience. This study concludes by highlighting areas where further research is needed and suggesting ways to create transparent and scalable ML and DL solutions that are suited to the evolving landscape of cybersecurity and digital forensics.
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