Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network

Shah Noor; Ahthasham Sajid; Imranullah Khan; Junaid Javaid; Iqra Tabasusum

doi:10.33317/ssurj.615

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Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network

Author(s)

Shah Noor Department of Computer Science, Faculty of ICT, BUITEMS,Quetta, Baluchistan,Pakistan

Ahthasham Sajid Department of Computer Science, Faculty of ICT, BUITEMS,Quetta, Baluchistan,Pakistan

Imranullah Khan College of Electrical Engineering/International College, Hebei University,Baoding,China

Junaid Javaid Department of Computer Science, Faculty of ICT, BUITEMS,Quetta, Baluchistan,Pakistan

Iqra Tabasusum Department of Computer Science, Faculty of ICT, BUITEMS,Quetta, Baluchistan,Pakistan

Abstract

Anomaly detection in a piece of data is a challenging task. Researchers use different approaches to classify data as anomalous. These include traditional, supervised, unsupervised, and semi-supervised techniques. A more recently introduced technique is Generative Adversarial Network (GAN), which is a deep learning-based technique. However, it is difficult to choose one anomaly detection algorithm over another because each algorithm stands out with its own performance. Therefore, this paper aims to provide a structured and comprehensive understanding of machine-learning-based anomaly detection techniques. This paper surveys the existing literature on machine-learning-based algorithms for anomaly detection. This paper places a special emphasis on Generative Adversarial Network-based algorithms for anomaly detection since it is the most widely used machine-learning-based algorithm for anomaly detection.

Publication Details

Page(s) 7-16

DOI 10.33317/ssurj.615

Published Journal: Sir Syed University Research Journal of Engineering and Technology, Volume: 13, Issue: 2, Year: 2023

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Keywords

deep learning, Intrusion Detection System, generative adversarial network, Anomaly Detection Techniques, Neural Networks.

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