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Research on data imbalance in intrusion detection using CGAN.

Guangyu Zhao¹, Peng Liu¹, Ke Sun²

¹School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China.

|October 10, 2023

Summary

This study introduces a novel intrusion detection strategy using conditional Generative Adversarial Networks (cGAN) to improve detection accuracy for unbalanced network data. The method enhances generalization ability and reduces missed attacks in intrusion detection systems (IDS).

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Area of Science:

Cybersecurity
Machine Learning
Network Security

Background:

Traditional Intrusion Detection Systems (IDS) struggle with unbalanced datasets, leading to attack category omission and poor generalization.
Processing imbalanced input data is a significant challenge in maintaining effective network security.

Purpose of the Study:

To propose a novel intrusion detection strategy addressing the limitations of traditional IDS in handling unbalanced data.
To enhance the generalization ability and reduce omission errors in intrusion detection.

Main Methods:

A conditional Generative Adversarial Network (cGAN) based strategy was developed for intrusion detection.
The cGAN generates synthetic attack samples that mimic the input data distribution within a bounded interval, avoiding data redundancy.
This approach aims to mitigate issues caused by mechanical data widening.

Main Results:

The proposed strategy demonstrated superior performance indexes compared to traditional methods.
Experimental results indicated a stronger generalization ability in overall performance.
The strategy effectively addressed insufficient classification performance and detection omission.

Conclusions:

The cGAN-based intrusion detection strategy offers a robust solution for unbalanced datasets.
This method significantly improves the detection of network intrusions and enhances system reliability.
The findings highlight the potential of cGANs in advancing the field of network intrusion detection.