Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems

Abstract

Due to the numerous advantages of machine learning (ML) algorithms, many applications now incorporate them. However, many studies in the field of image classification have shown that MLs can be fooled by a variety of adversarial attacks. These attacks take advantage of ML algorithms' inherent vulnerability. This raises many questions in the cybersecurity field, where a growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems, such as intrusion detection systems. The majority of this research demonstrates that it is possible to fool a model using features extracted from a raw data source, but it does not take into account the real implementation of such attacks, i.e., the reverse transformation from theory to practice. The real implementation of these adversarial attacks would be influenced by various constraints that would make their execution more difficult. As a result, the purpose of this study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems (NIDS), demonstrating that it is entirely possible to fool these ML-based IDSs using our proposed adversarial algorithm while assuming as many constraints as possible in a black-box setting. In addition, since it is critical to design defense mechanisms to protect ML-based IDSs against such attacks, a defensive scheme is presented. Realistic botnet traffic traces are used to assess this work. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.

This work is published in Computers & Security (an Elsevier journal) https://www.sciencedirect.com/science/article/pii/S016740482300086X

References in corpus (4)

• Adversarial Feature Selection against Evasion Attacks
• Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
• TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems
• Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection