A Wasserstein Gan Based Framework For Adversarial Attacks Against Intrusion Detection Systems
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A WASSERSTEIN GAN BASED FRAMEWORK FOR ADVERSARIAL ATTACKS AGAINST INTRUSION DETECTION SYSTEMS.
IIntrusion detection system (IDS) detects malicious activities in network flows and is essential for modern communication networks. Machine learning (ML) and deep learning (DL) have been employed to construct IDSs. However, the reliability of ML/DL-based IDSs is questionable under adversarial attacks. We propose a framework based on Wasserstein generative adversarial networks (WGANs) to generate adversarial traffic to evade ML/DL-based IDSs. The proposed framework involves restricted modification operations and the output of the framework is carefully regulated, preserving the functionality of the malicious attack. We also present a variant of the proposed framework based on conditional WGANs. The variant framework simplifies the training procedure without losing attack capability. Eight ML/DL-based IDSs are constructed, and their robustness against adversarial attacks is tested using the frameworks. The results show that the framework and its variant can generate adversaries effectively, and the Convolutional Neural Network has the best robustness under adversarial attacks.
Applied Soft Computing and Communication Networks
This book constitutes thoroughly refereed post-conference proceedings of the International Applied Soft Computing and Communication Networks (ACN 2023) held at PES University, Bangalore, India, during December 18–20, 2023. The research papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on security and privacy, network management and software-defined networks, Internet of Things (IoT) and cyber-physical systems, intelligent distributed systems, mobile computing and vehicle communications, and emerging topics. The book is directed to the researchers and scientists engaged in various fields of intelligent systems.
Advances in Knowledge Discovery and Data Mining
The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.