Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A New Data Balancing Approach based Generative Adversarial Network for Network Intrusion Detection System

Version 1 : Received: 30 May 2023 / Approved: 31 May 2023 / Online: 31 May 2023 (10:22:58 CEST)

A peer-reviewed article of this Preprint also exists.

Jamoos, M.; Mora, A.M.; AlKhanafseh, M.; Surakhi, O. A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System. Electronics 2023, 12, 2851. Jamoos, M.; Mora, A.M.; AlKhanafseh, M.; Surakhi, O. A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System. Electronics 2023, 12, 2851.

Abstract

The IDS serves as a security system that maintains constant surveillance over network traffic and host systems in order to identify any security breaches or potentially concerning activities. Recently. the rise in cyber-attacks has driven the necessity for the development of automated and intelligent network intrusion detection systems. These systems are designed to learn the typical patterns of network traffic, allowing them to identify any deviations from normal behaviour, which can be classified as anomalous or malicious. Machine learning methods are widely used to exhibit a satisfactory effectiveness in detecting malicious payloads in the network traffic. While the volume of the data generated from IDS is increasing exponentially results in the emergence of substantial security risks, it highlighted the imperative to strengthen network security. The performance of traditional machine learning methods depends on the dataset and the data balance distribution in it. while most of IDS datasets suffer from unbalancing, this limits the performance of the machine learning method used in the system and results in missed detections and false alarms in the conventional IDSs. To address this issue, this paper presents a new model-based Generative Adversarial Network (GAN) called TDCGAN to enhance the detection rate of less of minor class in the dataset while maintaining efficiency. The proposed model consists of one generator and three discriminators with an election layer at the end of architecture. The UGR’16 data set is used for evaluation purposes. In order to demonstrate the efficacy of our proposed model, various machine learning algorithms have been utilized for comparison. The experimental findings have determined that TDCGAN presents an efficient resolution for addressing imbalanced intrusion detection and surpasses the performance of other oversampling machine learning methods.

Keywords

Generative Adversarial Network; Intrusion Detection System; Imbalanced Dataset; Machine Learning; Unsupervised Learning

Subject

Computer Science and Mathematics, Security Systems

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.