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- Title
Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network.
- Authors
Noor, Shah; Sajid, Ahthasham; Khan, Imranullah; Javaid, Junaid; Tabasusum, Iqra
- 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.
- Subjects
GENERATIVE adversarial networks; ANOMALY detection (Computer security); INTRUSION detection systems (Computer security); DEEP learning; COMPARATIVE studies
- Publication
Sir Syed University Research Journal of Engineering & Technology (SSURJET), 2023, Vol 13, Issue 2, p8
- ISSN
1997-0641
- Publication type
Article
- DOI
10.33317/ssurj.615