A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems

• URL: http://arxiv.org/abs/2502.15561v1
• Date: Fri, 21 Feb 2025 16:22:11 GMT
• Title: A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
• Authors: Benyamin Tafreshian, Shengzhi Zhang,
• Abstract summary: Traditional signature-based Network Intrusion Detection Systems (NIDS) are inadequate against zero-day attacks.<n>Traditional signature-based NIDS are vulnerable to adversarial evasion attacks that subtly manipulate network traffic to bypass detection.<n>We propose a novel framework that enhances the robustness of ML-based NIDS by simultaneously integrating adversarial training, dataset balancing techniques, advanced feature engineering, ensemble learning, and extensive model fine-tuning.
• Score: 3.155715652244226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As cyberattacks become increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) are critical for modern network security. Traditional signature-based NIDS are inadequate against zero-day and evolving attacks. In response, machine learning (ML)-based NIDS have emerged as promising solutions; however, they are vulnerable to adversarial evasion attacks that subtly manipulate network traffic to bypass detection. To address this vulnerability, we propose a novel defensive framework that enhances the robustness of ML-based NIDS by simultaneously integrating adversarial training, dataset balancing techniques, advanced feature engineering, ensemble learning, and extensive model fine-tuning. We validate our framework using the NSL-KDD and UNSW-NB15 datasets. Experimental results show, on average, a 35% increase in detection accuracy and a 12.5% reduction in false positives compared to baseline models, particularly under adversarial conditions. The proposed defense against adversarial attacks significantly advances the practical deployment of robust ML-based NIDS in real-world networks.

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