Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust NIDS

• URL: http://arxiv.org/abs/2203.04234v1
• Date: Tue, 8 Mar 2022 17:52:09 GMT
• Title: Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust NIDS
• Authors: Jo\~ao Vitorino, Nuno Oliveira, Isabel Pra\c{c}a
• Abstract summary: Adrial attacks pose a major threat to machine learning and to the systems that rely on it. This work 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.
• Score: 0.3867363075280543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. Nonetheless, adversarial examples cannot be freely generated for domains with tabular data, such as cybersecurity. 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 developed 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 time efficient generation of realistic adversarial examples, which can be advantageous for both adversarial training and attacks.

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