Generative adversarial networks-enabled anomaly detection systems: A survey
Umer Saeed, Sana Ullah Jan, Jawad Ahmad, Syed Aziz Shah, Mohammed S. Alshehri, Yazeed Yasin Ghadi, Nikolaos Pitropakis, William J. Buchanan
Abstract
Anomaly Detection (AD) is an important area of research because it helps identify outliers in data, enabling early detection of errors, fraud, and potential security breaches. Machine Learning (ML) can be utilized for distinct AD systems, and Generative Adversarial Networks (GANs) have emerged as a promising technique due to their ability to generate new data that closely resembles a given dataset, allowing for the creation of realistic images, videos, audio, text, and other types of synthetic data. This paper explores state-of-the-art approaches in AD using GANs. The paper starts by providing a comprehensive overview of ML techniques for AD, including supervised, unsupervised, and semi-supervised approaches. This survey also explores various AD approaches based on GANs and provides an application-based classification of GANs-based AD approaches in the Internet-of-Things (IoT), Industrial IoT, Digital Healthcare, Energy Management Systems, and Cellular Network domains. Moreover, the paper discusses several datasets used in evaluating the performance of GANs-based AD techniques such as BOT-IoT, TON-IoT, CIC-IoT, CIC-IDS, and NSL-KDD. These datasets serve as valuable resources for researchers and practitioners to develop and test AD systems, particularly in the context of IoT and network security. Furthermore, the paper discusses the challenges and limitations of GANs-based AD techniques and proposes future research directions to address these challenges.