基于实时深度学习的网络入侵检测系统中结合启发式防御方法的无目标白盒对抗攻击研究
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System
AI总结：
本研究针对基于深度学习的实时网络入侵检测系统的对抗漏洞问题，实现了四种白盒对抗攻击并验证其效果，同时提出三种启发式防御策略提升系统鲁棒性，为网络安全领域的对抗机器学习研究提供参考。
摘要：
网络入侵检测系统（NIDS）是保障计算机网络免受各类网络安全威胁与网络攻击侵害的核心组件。然而，考虑到一种不利情形：NIDS本身也会遭受攻击且存在漏洞，更具体地说，我们可以提出这样的问题：如何防御防御者？在对抗机器学习（AML）领域中，恶意行为者旨在通过精心构造的对抗样本，欺骗机器学习（ML）与深度学习（DL）模型使其产生错误预测。这些添加了对抗扰动的样本已成为基于ML与DL的系统面临的最大漏洞，也是这类系统在NIDS等实时、任务关键型应用中落地部署的主要障碍。对抗机器学习是一个新兴研究领域，为保护计算机网络免受各类网络安全威胁，深入研究对抗攻击及其防御策略已成为必然要求。\n本研究旨在涵盖与NIDS、对抗攻击及其防御机制相关的重要内容，以提升基于ML与DL的NIDS的鲁棒性。我们在NIDS中实现了四种强效对抗攻击技术，分别是快速梯度符号法（FGSM）、雅可比显著性图攻击（JSMA）、投影梯度下降（PGD）以及Carlini & Wagner（C&W）攻击，并通过多种性能指标详细分析了其性能。此外，我们还实现了三种启发式防御策略，即对抗训练（AT）、高斯数据增强（GDA）以及高置信度（HC）防御，以提升NIDS在对抗攻击场景下的鲁棒性。整个工作流程在带有数据包流的实时网络中得到了验证。本研究为从计算机网络安全角度关注对抗机器学习及其实现的研究人员提供了全面的背景参考。
英文摘要：
Network Intrusion Detection System (NIDS) is a key component in securing the computer network from various cyber security threats and network attacks. However, consider an unfortunate situation where the NIDS is itself attacked and vulnerable more specifically, we can say, How to defend the defender?. In Adversarial Machine Learning (AML), the malicious actors aim to fool the Machine Learning (ML) and Deep Learning (DL) models to produce incorrect predictions with intentionally crafted adversarial examples. These adversarial perturbed examples have become the biggest vulnerability of ML and DL based systems and are major obstacles to their adoption in real-time and mission-critical applications such as NIDS. AML is an emerging research domain, and it has become a necessity for the in-depth study of adversarial attacks and their defence strategies to safeguard the computer network from various cyber security threads. In this research work, we aim to cover important aspects related to NIDS, adversarial attacks and its defence mechanism to increase the robustness of the ML and DL based NIDS. We implemented four powerful adversarial attack techniques, namely, Fast Gradient Sign Method (FGSM), Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini & Wagner (C&W) in NIDS. We analyzed its performance in terms of various performance metrics in detail. Furthermore, the three heuristics defence strategies, i.e., Adversarial Training (AT), Gaussian Data Augmentation (GDA) and High Confidence (HC), are implemented to improve the NIDS robustness under adversarial attack situations. The complete workflow is demonstrated in real-time network with data packet flow. This research work provides the overall background for the researchers interested in AML and its implementation from a computer network security point of view.
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