relation: https://khub.utp.edu.my/scholars/17275/
title: Deep Learning Model for Cybersecurity Attack Detection in Cyber-Physical Systems
creator: Abdullahi, M.
creator: Alhussian, H.
creator: Aziz, N.
creator: Abdulkadir, S.J.
creator: Baashar, Y.
description: In recent years, there has been an increasing demand for computing devices in cyber-physical systems (CPS), which include smart manufacturing, air intelligent transportation, critical infrastructure, robotic services, and Internet of Things (IoT) infrastructure. Field devices, on the other hand, such as sensors and actuators, which are frequently used for real-time monitoring and prediction, send a large amount of data through the network and communication layers. The CPS is vulnerable to major cybersecurity attacks. To overcome this, there's a need for new deep learning (DL) techniques that can investigate, detect, and respond to changes in such attacks. In this paper, we proposed a DL model for cyber security attack detection in the CPS based on long-short term memory (LSTM). Moreover, the model has been evaluated using real-world datasets from Industrial Control System (ICS) datasets of gas pipelines, which consist of seven attack types with 19 features. The results of the experiment show that the proposed model achieved an accuracy of 98.22 after validation. The paper also presents a recommendation for potential future investigation. © 2022 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Abdullahi, M. and Alhussian, H. and Aziz, N. and Abdulkadir, S.J. and Baashar, Y.  (2022) Deep Learning Model for Cybersecurity Attack Detection in Cyber-Physical Systems.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147418851&doi=10.1109%2fICCUBEA54992.2022.10010717&partnerID=40&md5=190539c9921add8ba8776aadeab49d99
relation: 10.1109/ICCUBEA54992.2022.10010717
identifier: 10.1109/ICCUBEA54992.2022.10010717