Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks
| Autor: | Ehsan Nowroozi, Mohammadreza Mohammadi, Erkay Savaş, Yassine Mekdad, Mauro Conti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Cryptography and Security Cybersecurity Computer Science - Artificial Intelligence Computer Networks and Communications Ensemble Classifiers Machine Learning (cs.LG) Computer Science - Networking and Internet Architecture Secure Classification Computer security Counter-Forensics Training Adversarial Attacks Adversarial Examples Adversarial Machine Learning Computer architecture Computer networks Convolutional neural networks Deep-Learning Security Forensics Support vector machines Electrical and Electronic Engineering Networking and Internet Architecture (cs.NI) Artificial Intelligence (cs.AI) Cryptography and Security (cs.CR) |
| Popis: | 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 such 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 (SPRITZ-1.5C) classifier and 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 SPRITZ-1.5C 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 SPRITZ-1.5C classifier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|