Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems

Abstract

The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning, we identify and model the real capabilities and circumstances required by attackers to carry out feasible and successful adversarial attacks. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this paper can help hardening defensive systems by letting cyber defenders address the most critical and real issues, and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.

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Cited by in corpus (12)

• The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
• Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
• Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems
• Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification
• Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
• Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
• Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
• Exploring Robust Architectures for Deep Artificial Neural Networks
• PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis
• An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
• Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networks
• Defending Network Intrusion Detection Systems Based on Graph Neural Networks Against Structural Adversarial Attacks