Emerging framework for attack detection in cyber-physical systems using heuristic-based optimization algorithm

Manal Abdullah Alohali; Muna Elsadig; Anwer Mustafa Hilal; Abdulwahed Mutwakel

doi:10.7717/peerj-cs.1596

Emerging framework for attack detection in cyber-physical systems using heuristic-based optimization algorithm

PeerJ Comput Sci. 2023 Dec 6:9:e1596. doi: 10.7717/peerj-cs.1596. eCollection 2023.

Authors

Manal Abdullah Alohali¹, Muna Elsadig¹, Anwer Mustafa Hilal², Abdulwahed Mutwakel³

Affiliations

¹ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
² Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, Saudi Arabia, Saudi Arabia, Saudi Arabia.
³ Department of Information Systems, Prince Sattam bin Abdulaziz University, Saudi Arabia, Saudi Arabia, Saudi Arabia.

Abstract

In recent days, cyber-physical systems (CPS) have become a new wave generation of human life, exploiting various smart and intelligent uses of automotive systems. In these systems, information is shared through networks, and data is collected from multiple sensor devices. This network has sophisticated control, wireless communication, and high-speed computation. These features are commonly available in CPS, allowing multi-users to access and share information through the network via remote access. Therefore, protecting resources and sensitive information in the network is essential. Many research works have been developed for detecting insecure networks and attacks in the network. This article introduces a framework, namely Deep Bagging Convolutional Neural Network with Heuristic Multiswarm Ant Colony Optimization (DCNN-HMACO), designed to enhance the secure transmission of information, improve efficiency, and provide convenience in Cyber-Physical Systems (CPS). The proposed framework aims to detect attacks in CPS effectively. Compared to existing methods, the DCNN-HMACO framework significantly improves attack detection rates and enhances overall system protection. While the accuracy rates of CNN and FCM are reported as 72.12% and 79.56% respectively, our proposed framework achieves a remarkable accuracy rate of 92.14%.

Keywords: Ant colony optimization; CNN; Cyber-physical system; Deep bagging; Heuristic.

Grants and funding

This work was supported by the Deanship for Research & Innovation, Ministry of Education in Saudi Arabia through the project number RI-44-0658. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.