DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915249840455680 |
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| author | Chen, Jing Gungor, Onat Shang, Zhengli Li, Elvin Rosing, Tajana |
| author_facet | Chen, Jing Gungor, Onat Shang, Zhengli Li, Elvin Rosing, Tajana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks Chen, Jing Gungor, Onat Shang, Zhengli Li, Elvin Rosing, Tajana Cryptography and Security 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. |
| title | DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2504.13301 |