Volume 61, Issue 337, January - April 2025

Moderation of cyber attacks in IoTs using deep learning techniques

Ifeanyi Stanly Nwokoro¹, Shafi’I M Abdulhamid², Kwaku Zacciah Adom-Oduro³, Anayo Chukwu Ikegwu⁴, Augustina Nebechi Nwatu⁵

¹Department of Computer Science, Rhema University, Nigeria
²Department of Cyber Security, National Open University of Nigeria
³Department of Computer Science, Technical University, Ghana
⁴Department of Software Engineering, Veritas University, Nigeria
⁵Department of Biotechnology, Alex Ekwueme Federal University (AE-FUNAI), Nigeria

ABSTRACT

The widespread use of IoT devices in the modern day has simplified our lives and elevated our everyday routines to a new level. IoT devices are connected to communicate and share data with gateways or access points (APs) for additional data processing. On the other hand, this makes cybersecurity and zero-day assaults in IoT networks more prevalent. Deep learning models and datasets used to identify fraudulent data in IoT environments have been reviewed in this research. In the context of the Internet of Things, we found that the Long Short-Term Memory (LSTM), Convolution Neural Network (CNN) and stacking auto-encoders improve the accuracy and precision of malicious packet detection. We undertook a thorough theoretical examination of deep learning datasets and models. Our research finding serves as paradigm to researchers as a technique to investigate IoT security and privacy challenges.

Keywords: Internet of Things (IOTs); Access Points (APs); Long Short-Term Memory (LSTM); Convolution Neural Network (CNN); Deep Learning.

Discovery, 2025, 61, e7d1519

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DOI: https://doi.org/10.54905/disssi.v61i337.e7d1519

Published: 31 January 2025