梳理计算机网络中对抗攻击迁移性的校正问题
Unscrambling the Rectification of Adversarial Attacks Transferability across Computer Networks
AI总结：
该研究针对CNN在网络安全等领域的对抗攻击脆弱性，提出评估攻击强度变化下对抗样本迁移性的方法，验证了多种攻击在计算机网络数据集上的迁移性提升，凸显了研发新型防御机制的必要性。
摘要：
卷积神经网络（CNN）模型在众多技术领域实现最先进性能方面发挥着关键作用。CNN的应用不仅限于自然语言处理（NLP）或计算机视觉（CV），在其他技术领域也有着大量应用，尤其是在网络安全领域。由于CNN模型易受对抗攻击影响，其可靠性可能会受损；这类对抗攻击生成难度低、实施简便，且能在现实场景中实现迁移。\n本文提出了一种新颖且全面的方法，用于提升攻击强度，评估攻击强度变化时CNN中对抗样本的迁移性，以及计算机网络应用中是否存在迁移性问题。在本研究中，我们首先考察了六种不同的攻击模式：Carlini and Wagner（C&W）攻击、快速梯度符号法（Fast Gradient Sign Method, FGSM）、迭代快速梯度符号法（Iterative Fast Gradient Sign Method, I-FGSM）、基于雅可比的显著性图攻击（Jacobian-based Saliency Map, JSMA）、有限内存布罗伊登-弗莱彻-戈德法布-香农（Limited-memory Broyden fletcher Goldfarb Shanno, L-BFGS）攻击以及投影梯度下降（Projected Gradient Descent, PGD）攻击。我们将这些攻击技术应用于两个常用数据集：CIC数据集和UNSW数据集。实验结果表明，在目标场景下，FGSM、JSMA、LBFGS等攻击的迁移性均有所提升。我们的研究结果进一步表明，即便在计算机网络应用中，对抗样本带来的安全威胁也要求我们开发新型防御机制，以提升基于深度学习的技术的安全性。
英文摘要：
Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances in various technological fields. CNNs are not limited to Natural Language Processing (NLP) or Computer Vision (CV) but also have substantial applications in other technological domains, particularly in cybersecurity. The reliability of CNN's models can be compromised because of their susceptibility to adversarial attacks, which can be generated effortlessly, easily applied, and transferred in real-world scenarios. In this paper, we present a novel and comprehensive method to improve the strength of attacks and assess the transferability of adversarial examples in CNNs when such strength changes, as well as whether the transferability property issue exists in computer network applications. In the context of our study, we initially examined six distinct modes of attack: the Carlini and Wagner (C&W), Fast Gradient Sign Method (FGSM), Iterative Fast Gradient Sign Method (I-FGSM), Jacobian-based Saliency Map (JSMA), Limited-memory Broyden fletcher Goldfarb Shanno (L-BFGS), and Projected Gradient Descent (PGD) attack. We applied these attack techniques on two popular datasets: the CIC and UNSW datasets. The outcomes of our experiment demonstrate that an improvement in transferability occurs in the targeted scenarios for FGSM, JSMA, LBFGS, and other attacks. Our findings further indicate that the threats to security posed by adversarial examples, even in computer network applications, necessitate the development of novel defense mechanisms to enhance the security of DL-based techniques.
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