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

2025-02-21

AI Summary

To address the insufficient robustness of machine learning-based network intrusion detection systems (ML-based NIDS) against adversarial evasion attacks, this paper proposes an end-to-end robust defense framework integrating adversarial training, SMOTE-based class balancing, information gain-based feature selection, XGBoost-LSTM ensemble modeling, and multi-stage fine-tuning. This work is the first to synergistically combine these five techniques within the NIDS domain, significantly enhancing model generalization and stability against perturbed traffic. Experimental evaluation on NSL-KDD and UNSW-NB15 demonstrates an average 35% improvement in detection accuracy, a 12.5% reduction in false positive rate, and strong robustness across diverse adversarial attack scenarios. The proposed framework establishes a systematic, holistic defense paradigm for building trustworthy ML-based NIDS.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Enhancing ML-based NIDS robustness
• Defending against adversarial evasion attacks
• Improving detection accuracy and reducing false positives

Innovation

• Adversarial training enhances robustness
• Ensemble learning improves detection accuracy
• Feature engineering reduces false positives