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arXiv 2501.03250cs.CRcs.AI

网络安全与数字取证中使用的机器学习和深度学习技术:综述

Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review

Jaouhar Fattahi

AI总结:

本文综述 ML 与 DL 在网络安全和数字取证中的应用,分析其优劣、技术场景及未来研究方向。

摘要:

在网络安全和数字取证的快速发展领域中,机器学习(ML)和深度学习(DL)已成为改变局面的技术,提供了识别、阻止和分析网络风险的方法。本综述概述了这些领域中使用的 ML 和 DL 方法,展示了它们的优势、缺点和可能性。其内容涵盖用于发现系统入侵、对恶意软件进行分类以预防网络安全攻击、检测异常并增强韧性的一系列 AI 技术。本研究最后强调了需要进一步研究的领域,并提出相关方法,以构建透明、可扩展且适应网络安全与数字取证不断变化格局的 ML 和 DL 解决方案。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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