DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks

• URL: http://arxiv.org/abs/2504.13301v1
• Date: Thu, 17 Apr 2025 19:22:14 GMT
• Title: DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
• Authors: Jing Chen, Onat Gungor, Zhengli Shang, Elvin Li, Tajana Rosing,
• Abstract summary: Internet of Things (IoT) has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems (IDS)<n>We propose Dynamite, a dynamic defense selection framework that enhances ML-IDS by intelligently identifying and deploying the most suitable defense using a machine learning-driven selection mechanism.<n>Our results demonstrate that Dynamite achieves a 96.2% reduction in computational time compared to the Oracle, significantly decreasing computational overhead while preserving strong prediction performance.
• Score: 8.329676508547509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid proliferation of the Internet of Things (IoT) has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems (IDS). Machine learning-based intrusion detection systems (ML-IDS) have significantly improved threat detection capabilities; however, they remain highly susceptible to adversarial attacks. While numerous defense mechanisms have been proposed to enhance ML-IDS resilience, a systematic approach for selecting the most effective defense against a specific adversarial attack remains absent. To address this challenge, we propose Dynamite, a dynamic defense selection framework that enhances ML-IDS by intelligently identifying and deploying the most suitable defense using a machine learning-driven selection mechanism. Our results demonstrate that Dynamite achieves a 96.2% reduction in computational time compared to the Oracle, significantly decreasing computational overhead while preserving strong prediction performance. Dynamite also demonstrates an average F1-score improvement of 76.7% over random defense and 65.8% over the best static state-of-the-art defense.

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