Title: A hybrid machine learning method in detecting anomalies in IoT at the fog layer

Authors: Believe Ayodele; Michaela Tromans Jones

Addresses: Research and Innovation Centre, Malta College of Arts and Science, Triq Kordin Paola, PLA9032, Malta ' Research and Innovation Centre, Malta College of Arts and Science, Triq Kordin Paola, PLA9032, Malta

Abstract: With the rapid growth and utilisation of IoT devices around the world, attacks on these devices are also increasing, thereby posing a security and privacy issue for industry providers and end-users alike. A common way to detect anomaly behaviour is to analyse the network traffic and categorise the outcome into benign and malignant traffic. With an increase in network traffic and sophistication of attacking techniques daily, there is a need for a state-of-the-art pattern recognition technique that can handle this ever-increasing and ever-changing traffic and can also improve over time as attacks become more sophisticated. This research paper proposes a hybrid model for anomaly detection at the IoT fog layer using an ANN as a base model and several binary classifiers. The proposed model was tested and evaluated on a dataset, demonstrating that such a model is both highly effective and efficient in detecting IoT network traffic anomalies.

Keywords: internet of things; IoT; artificial neural network; ANN; machine learning; ML; binary classifiers; anomaly detection.

DOI: 10.1504/IJITCA.2022.124363

International Journal of Internet of Things and Cyber-Assurance, 2022 Vol.2 No.1, pp.1 - 30

Received: 10 Nov 2021
Accepted: 19 Dec 2021
Published online: 25 Jul 2022 *

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