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

TL;DR

This paper introduces a comprehensive defensive framework that significantly improves the robustness of machine learning-based network intrusion detection systems against adversarial attacks, enhancing detection accuracy and reducing false positives.

Contribution

It presents a novel integrated defense approach combining adversarial training, dataset balancing, feature engineering, ensemble learning, and fine-tuning for robust ML-based NIDS.

Findings

35% increase in detection accuracy

12.5% reduction in false positives

Effective under adversarial conditions

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.