SGAN-IDS:针对入侵检测系统的基于自我注意的生成对抗网络
Sahar Aldhaheri1, Abeer Alhuzali1
1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了SGAN-IDS,这是一个创建对抗网络流量的框架,以绕过基于机器学习的入侵检测系统. 开发的对抗性攻击成功地逃避了检测,突出了当前系统的漏洞.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 网络入侵检测系统 (NIDS) 对于监控网络流量至关重要.
- 现有的基于机器学习的NIDS (ML-NIDS) 研究往往缺乏现实的实验设置.
- 对抗新型零日和对抗性攻击的稳定性是NIDS的关键,未经探索的领域.
研究的目的:
- 开发一个框架 (SGAN-IDS) 来构建对抗性攻击流.
- 评估这些流量的有效性与基于BlackBox ML的入侵检测系统 (IDS) 相比.
- 评估拟议模型在规避NIDS方面的稳定性和适用性.
主要方法:
- 开发了SGAN-IDS框架,利用生成对抗网络和自我注意机制.
- 生成的合成对抗性攻击流,旨在规避基于ML的IDS.
- 对五种不同的基于BlackBox ML的IDS进行了SGAN-IDS评估.
主要成果:
- SGAN-IDS成功地为各种攻击类型生成了对抗性流.
- 生成的对抗性流量使所有测试的基于ML的IDS的检测率平均降低了15.93%.
- 证明了该模型能够创建弹性和广泛适用的对抗性攻击的能力.
结论:
- SGAN-IDS框架有效地产生了对抗性攻击流,挑战了当前的ML-NIDS.
- 这些发现凸显了现有的ML-NIDS对复杂的逃避技术的脆弱性.
- 强调需要更强大的NIDS防御对抗对方的攻击.
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