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

Benyamin Tafreshian, Shengzhi Zhang

TL;DR

The paper addresses the vulnerability of ML-based NIDS to adversarial evasion by proposing a defense that jointly uses adversarial training, dataset balancing, protocol-aware feature engineering, ensemble learning, and extensive fine-tuning. It introduces a realistic adversarial attack generation method based on a genetic algorithm that respects network protocols and feature interdependencies, and evaluates the framework on NSL-KDD and UNSW-NB15. Results show a substantial average improvement in detection accuracy (+~35%) and a reduction in false positives (-~12.5%), especially under adversarial conditions, demonstrating the framework's practical robustness. The work advances robust deployment of ML-based NIDS by tightly integrating attack-aware defense design with domain-specific traffic considerations and scalable training approaches.

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.

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

TL;DR

The paper addresses the vulnerability of ML-based NIDS to adversarial evasion by proposing a defense that jointly uses adversarial training, dataset balancing, protocol-aware feature engineering, ensemble learning, and extensive fine-tuning. It introduces a realistic adversarial attack generation method based on a genetic algorithm that respects network protocols and feature interdependencies, and evaluates the framework on NSL-KDD and UNSW-NB15. Results show a substantial average improvement in detection accuracy (+~35%) and a reduction in false positives (-~12.5%), especially under adversarial conditions, demonstrating the framework's practical robustness. The work advances robust deployment of ML-based NIDS by tightly integrating attack-aware defense design with domain-specific traffic considerations and scalable training approaches.

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.

Paper Structure

This paper contains 21 sections, 2 equations, 3 tables.

Table of Contents

  1. Introduction
  2. Background Information
  3. ML-based NIDS
  4. Overview of Adversarial Attacks on ML-based NIDS
  5. Flows, Packets, and ML-Based Pattern Detection
  6. Related Works
  7. Proposed Defensive Mechanisms for ML-based NIDS
  8. Adversarial Attack Generation and Evasion Techniques
  9. Attack Transferability Across Machine Learning Models
  10. Adversarial Attack Generation Methodology
  11. Building Robust NIDS against Adversarial Attacks
  12. Adversarial Training
  13. Feature Extraction, Selection, and Engineering
  14. Feature Extraction and Selection
  15. Feature Engineering for Security
  16. ...and 6 more sections