针对基于dl的NIDS的对抗性示例的现实性与性能

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Huda Ali Alatwi, C. Morisset
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引用次数: 0

摘要

基于深度学习(DL)的网络入侵检测系统(NIDS)的应用能够有效地自动检测网络攻击。该模型可以以最小的特征工程从高维异构网络流量中提取有价值的特征,并提供较高的准确率检测率。然而,已有研究表明,深度学习可能容易受到对抗性示例(AEs)的影响,这些示例会在推理时误导分类决策,并且一些研究表明,AEs确实是对基于DL的NIDS的威胁。在这项工作中,我们认为这些威胁并不一定是现实的。实际上,一些用于生成AE的通用技术以一种与实际网络流量不一致的方式操作特征。在本文中,我们首先针对两个不同的数据集(WSN-DS和BoT-IoT)实现了从文献中选择的主要AE攻击(FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO),并比较了它们的相对性能。然后,我们分析由这些攻击产生的扰动,并使用度量来建立“攻击非现实性”的概念。我们得出的结论是,对于这些数据集,其中一些攻击是有效的,但不现实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Realism versus Performance for Adversarial Examples Against DL-based NIDS
The application of deep learning-based (DL) network intrusion detection systems (NIDS) enables effective automated detection of cyberattacks. Such models can extract valuable features from high-dimensional and heterogeneous network traffic with minimal feature engineering and provide high accuracy detection rates. However, it has been shown that DL can be vulnerable to adversarial examples (AEs), which mislead classification decisions at inference time, and several works have shown that AEs are indeed a threat against DL-based NIDS. In this work, we argue that these threats are not necessarily realistic. Indeed, some general techniques used to generate AE manipulate features in a way that would be inconsistent with actual network traffic. In this paper, we first implement the main AE attacks selected from the literature (FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO) for two different datasets (WSN-DS and BoT-IoT) and we compare their relative performance. We then analyze the perturbation generated by these attacks and use the metrics to establish a notion of "attack unrealism". We conclude that, for these datasets, some of these attacks are performant but not realistic.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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