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Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative

Andrei-Grigore Mari¹, Daniel Zinca¹, Virgil Dobrota¹

¹Communications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces generative adversarial networks (GANs) to create adversarial network traffic for improving intrusion detection systems (IDSs). GAN-generated traffic can evade detection, but using it for testing enhances IDS performance against new attacks.

Keywords:

NSL-KDD dataset Python generative adversarial network intrusion detection system intrusion evasion machine learning

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Area of Science:

Cybersecurity
Network Security
Machine Learning

Background:

Intrusion detection and prevention are critical for network security infrastructure.
Machine learning-based Intrusion Detection Systems (IDSs) are increasingly used to detect sophisticated malicious traffic.
Attackers continuously evolve methods to evade rule-based detection systems.

Purpose of the Study:

To demonstrate the creation of adversarial network traffic capable of evading machine learning-based IDSs.
To implement a Generative Adversarial Network (GAN) for generating such adversarial traffic.
To evaluate the impact of using GAN-generated adversarial traffic on IDS performance.

Main Methods:

Utilized the NSL-KDD dataset for training and evaluating machine learning models.
Developed a Generative Adversarial Network (GAN), a deep learning architecture, to create adversarial traffic instances.
Tested the performance of an IDS against GAN-generated adversarial traffic.

Main Results:

GAN-generated adversarial traffic was successfully created and demonstrated the ability to evade IDS detection.
Testing the IDS with the generated adversarial traffic led to improved IDS performance.
The study confirmed that adversarial traffic can be used to enhance IDS resilience against novel attacks.

Conclusions:

Generative Adversarial Networks (GANs) offer a powerful method for creating sophisticated adversarial network traffic.
Adversarial training using GAN-generated data can significantly improve the robustness and detection capabilities of machine learning-based IDSs.
This approach provides a viable strategy for proactively defending against evolving cyber threats.