Adversarial attacks based on time-series features for traffic detection

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Computers & Security Pub Date : 2024-10-29 DOI:10.1016/j.cose.2024.104175
Hongyu Lu, Jiajia Liu, Jimin Peng, Jiazhong Lu
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Abstract

To enhance the robustness of intrusion detection classifiers, we propose a Time Series-based Adversarial Attack Framework (TSAF) targeting the temporal characteristics of network traffic. Initially, adversarial samples are generated using the gradient calculations of CNNs, with updates iterated based on model loss. Different attack schemes are then applied to various traffic types and saved as generic adversarial perturbations. These time series-based perturbations are subsequently injected into the traffic stream. To precisely implement the adversarial perturbations, a masking mechanism is utilized. Our adversarial sample model was evaluated, and the results indicate that our samples can reduce the accuracy and recall rates for detecting four types of malicious network traffic, including botnets, brute force, port scanning, and web attacks, as well as degrade the detection performance of DDoS traffic. The CNN model’s accuracy dropped by up to 72.76%, and the SDAE model’s accuracy by up to 78.77% with minimal perturbations. Our adversarial sample attack offers a new perspective in the field of cybersecurity and lays the groundwork for designing AI models that can resist adversarial attacks more effectively.
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基于时间序列特征的流量检测对抗攻击
为了增强入侵检测分类器的鲁棒性,我们针对网络流量的时间特征提出了基于时间序列的对抗攻击框架(TSAF)。最初,利用 CNN 的梯度计算生成对抗样本,并根据模型损失迭代更新。然后将不同的攻击方案应用于各种流量类型,并保存为通用对抗扰动。这些基于时间序列的扰动随后会被注入到流量流中。为了精确实施对抗扰动,我们采用了一种屏蔽机制。对我们的对抗样本模型进行了评估,结果表明我们的样本可以降低四种恶意网络流量(包括僵尸网络、暴力破解、端口扫描和网络攻击)的检测准确率和召回率,并降低 DDoS 流量的检测性能。CNN 模型的准确率下降了 72.76%,SDAE 模型的准确率下降了 78.77%。我们的对抗性样本攻击为网络安全领域提供了一个新视角,并为设计能更有效抵御对抗性攻击的人工智能模型奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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