Employing deep ensemble learning for improving the security of computer networks against adversarial attacks

Nowroozi, Ehsan and Mohammadi, Mohammadreza and Savaş, Erkay and Mekdad, Yassine and Conti, Mauro (2023) Employing deep ensemble learning for improving the security of computer networks against adversarial attacks. IEEE Transactions on Network and Service Management, 20 (2). pp. 2096-2105. ISSN 1932-4537

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In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications, including computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1.5-Class (cmb-classifier) classifier and is constructed using a final dense classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and cmb-classifier architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the cmb-classifier.
Item Type: Article
Uncontrolled Keywords: Adversarial Attacks; Adversarial Examples; Adversarial Machine Learning; Computer architecture; Computer networks; Computer security; Convolutional neural networks; Counter-Forensics; Cybersecurity; Deep-Learning Security; Ensemble Classifiers; Forensics; Secure Classification; Support vector machines; Training
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Erkay Savaş
Date Deposited: 05 Aug 2023 16:36
Last Modified: 05 Aug 2023 16:36
URI: https://research.sabanciuniv.edu/id/eprint/47219

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