Multi-step attack detection in industrial networks using a hybrid deep learning architecture
-
Published:2023
Issue:8
Volume:20
Page:13824-13848
-
ISSN:1551-0018
-
Container-title:Mathematical Biosciences and Engineering
-
language:
-
Short-container-title:MBE
Author:
Jamal Muhammad Hassan1, Khan Muazzam A12, Ullah Safi1, Alshehri Mohammed S.3, Almakdi Sultan3, Rashid Umer1, Alazeb Abdulwahab3, Ahmad Jawad4
Affiliation:
1. Department of Computer Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan 2. ICESCO Chair Big Data Analytics and Edge Computing, Quaid-i-Azam University, Islamabad 45320, Pakistan 3. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia 4. School of Computing, Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT, Edinburgh, UK
Abstract
<abstract><p>In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information. To ensure the effectiveness of detection in industrial networks, which necessitate continuous operation and undergo changes over time, intrusion detection algorithms should possess the capability to automatically adapt to these changes. Several researchers have focused on the automatic detection of these attacks, in which deep learning (DL) and machine learning algorithms play a prominent role. This study proposes a hybrid model that combines two DL algorithms, namely convolutional neural networks (CNN) and deep belief networks (DBN), for intrusion detection in industrial networks. To evaluate the effectiveness of the proposed model, we utilized the Multi-Step Cyber Attack (MSCAD) dataset and employed various evaluation metrics.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference67 articles.
1. R. M. Balajee, M. K. J. Kannan, Intrusion detection on AWS cloud through hybrid deep learning algorithm, Electronics, 12 (2023), 1423. https://doi.org/10.3390/electronics12061423 2. M. J. Kaur, V. P. Mishra, P. Maheshwari, The convergence of digital twin, IoT, and machine learning: transforming data into action, in Digital Twin Technologies and Smart Cities, Springer, (2020), 3–17. https://link.springer.com/chapter/10.1007/978-3-030-18732-3_1 3. O. Abualghanam, H. Alazzam, B. Elshqeirat, M. Qatawneh, M. A. Almaiah, Real-time detection system for data exfiltration over DNS tunneling using machine learning, Electronics, 12 (2020), 1467. https://doi.org/10.3390/electronics12061467 4. B. Axelsson, G. Easton, Industrial Networks (Routledge Revivals): A New View of Reality, Routledge, 1992. 5. P. C. Smith, L. Hellman, Small Group Analysis in Industrial Networks, Routledge, 1992.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|