Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

TL;DR

This paper introduces the Adaptative Perturbation Pattern Method (A2PM), a novel approach for generating realistic adversarial examples in cybersecurity, enhancing the robustness of intrusion detection systems against evasion attacks.

Contribution

The paper proposes A2PM, a new method that creates realistic adversarial examples by adapting perturbation patterns to class characteristics in a gray-box setting, addressing realism constraints.

Findings

A2PM generates scalable, realistic adversarial examples.

A2PM improves adversarial training effectiveness.

Adversarial examples successfully evade classifiers in case studies.

Abstract

Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic within that domain. This work establishes the fundamental constraint levels required to achieve realism and introduces the Adaptative Perturbation Pattern Method (A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on pattern sequences that are independently adapted to the characteristics of each class to create valid and coherent data perturbations. The proposed method was evaluated in a cybersecurity case study with two scenarios: Enterprise and Internet of Things (IoT) networks. Multilayer Perceptron (MLP) and Random Forest (RF) classifiers were created with regular and adversarial training, using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and untargeted attacks were performed against the classifiers, and the generated examples were compared with the original network traffic flows to assess their realism. The obtained results demonstrate that A2PM provides a scalable generation of realistic adversarial examples, which can be advantageous for both adversarial training and attacks.