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.
View on arXiv@article{chen2025_2504.13301,
title={ DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks },
author={ Jing Chen and Onat Gungor and Zhengli Shang and Elvin Li and Tajana Rosing },
journal={arXiv preprint arXiv:2504.13301},
year={ 2025 }
}