Work

Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems

Year: 2024

Type: article

Abstract: Securing Internet of Things (IoT)-enabled Cyber-Physical Systems (CPSs) can be challenging because security solutions intended for typical IT/OT systems may not be as effective in a CPS setting. The goal of this study is to create a mechanism for identifying and attributing two-level ensemble attacks that are specifically designed for use against Industrial Control Systems (ICSs). An original ensemble deep representation learning model is combined with decision tree algorithm to identify assaults on unbalanced ICS environments at the first level. An attack attribution network, which constitutes a collection of deep neural networks, is formed at the second level. The proposed model is tested using real-world datasets, notably those pertaining to water purification and gas pipelines. The results demonstrate that the proposed strategy outperforms other strategies with comparable computing complexity and that the recommended model outperforms the existing mechanisms.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.6969

Authors: C. Atheeq, Ruhiat Sultana, Syeda Asfiya Sabahath, Murtuza Ahmed Khan Mohammed

Institutions: GITAM University, King Khalid University, University of Technology Malaysia

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $431