Internet of Things Anomaly Detection Enhancement using GAN-based Data Augmentation

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

Anomaly detection in Internet of Things (IoT) systems is essential for preventing failures, security breaches, and inaccurate predictions. Deep learning-based approaches are effective for this purpose but often suffer from insufficient labeled training data, which limits detection accuracy. To address this challenge, we propose a novel anomaly detection framework that embeds synthetic data generation with the anomaly detection process. The framework integrates a Generative Adversarial Network (GAN)-based module that learns underlying patterns in normal and/or abnormal data to generate synthetic samples, effectively augmenting the training set. This enriched dataset allows the anomaly detection module to better capture data variability and improve accuracy. For anomaly detection, raw data is first converted into spectrogram images to enhance feature representation, which are then encoded by a pre-trained Convolutional Neural Network (CNN) for effective feature extraction. These features are passed to one of two detection models: a deep Autoencoder, which learns representations of normal data in an unsupervised manner and identifies anomalies by reconstruction error, or a CNN classifier, which uses supervised learning to classify normal and abnormal data. Experimental results show that GAN-based data augmentation significantly boosts detection performance in both the supervised and unsupervised learning cases, making this approach highly effective for reliable anomaly detection in IoT systems.
Original languageEnglish
Title of host publication2024 17th International Conference on Signal Processing and Communication System (ICSPCS)
EditorsMehran Abolhasan, Jerzy Lopatka, Tomasz Marciniak, Hamid Sharif, Beata J Wyaocki, Hans-Juergen Zepernick, Margaret Lech, Rodney Kennedy, John McEachen, Parastoo Sadeghi, Tadeusz A Wysocki, John Leis, Tomasz Talaśka, Olutayo Oyerinde, James Scrofani, Peter Vial
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
ISBN (Print)9798350389647
DOIs
Publication statusPublished - 18 Dec 2024
Event2024 17th International Conference on Signal Processing and Communication System (ICSPCS) - Surfers Paradise, Australia, Surfers Paradise, Australia
Duration: 16 Dec 202418 Dec 2024

Conference

Conference2024 17th International Conference on Signal Processing and Communication System (ICSPCS)
Abbreviated titleICSPC 2024
Country/TerritoryAustralia
CitySurfers Paradise
Period16/12/2418/12/24

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