RobEns:强大的集体对抗机器学习框架,用于保护物联网流量
Sarah Alkadi1, Saad Al-Ahmadi1, Mohamed Maher Ben Ismail1
1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究介绍了RobEns,这是一个强大的整体框架,用于保护物联网 (IoT) 入侵检测系统 (IDS) 免受对抗机器学习 (AML) 攻击. RobEns增强了IDS安全性,并保持了高准确性,即使在复杂的逃避策略下.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 基于机器学习 (ML) 的入侵检测系统 (IDS) 对物联网 (IoT) 安全至关重要.
- 这些基于机器学习的IDS容易受到通过对抗机器学习 (AML) 攻击产生的对抗实例的攻击.
- 攻击者利用这些漏洞降低IDS性能并逃避检测,需要强大的防御策略.
研究的目的:
- 引入RobEns,这是一个强大的整体框架,用于增强物联网网络中的基于ML的IDS.
- 调查黑子AML逃避攻击对各种基于ML的IDS的影响.
- 在实施防御机制后评估这些IDS的稳定性.
主要方法:
- 开发了RobEns,这是一个整体框架,集成了最先进的ML模型和物联网IDS的整体技术.
- 在多类分类场景中使用三个基准测试数据集对六个基于ML的IDS进行了四次典型的AML攻击.
- 实施了两个防御机制:特征挤压 (基于数据) 和对抗训练 (基于模型).
主要成果:
- 实验显示,在AML攻击下,一些基于ML的IDS的检测准确度显著下降.
- 实施的防御机制 (功能挤压和对抗训练) 显著提高了IDS的稳定性.
- 在黑子攻击场景中,RobEns在没有对手的情况下实现了高达100%的准确性,同时保持了高性能.
结论:
- RobEns框架有效地提高了基于ML的IDS对物联网环境中的AML攻击的稳定性.
- 防御策略,如特征挤压和对抗训练,对于加强IDS至关重要.
- 拟议的方法通过在面对复杂威胁时保持高检测准确度来确保安全的物联网网络.
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