Work

Moderation of cyber attacks in IoTs using deep learning techniques

Year: 2025

Type: article

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.

Source: Discovery

DOI: 10.54905/disssi.v61i337.e7d1519

Authors: Ifeanyi Stanly Nwokoro, Shafi’I M Abdulhamid, Kwaku Zacciah Adom-Oduro, Anayo Chukwu Ikegwu, Augustina Nebechi Nwatu

Institutions: Rhema University, National Open University of Nigeria, Veritas University, Alex Ekwueme Federal University, Ndufu-Alike

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4409973356

FWCI:

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