Multi-step attack detection in industrial networks using a hybrid deep learning architecture

被引:4
作者
Jamal, Muhammad Hassan [1 ]
Khan, Muazzam A. [1 ,2 ]
Ullah, Safi [1 ]
Alshehri, Mohammed S. [3 ]
Almakdi, Sultan [3 ]
Rashid, Umer [1 ]
Alazeb, Abdulwahab [3 ]
Ahmad, Jawad [4 ]
机构
[1] Quaid i Azam Univ, Dept Comp Sci, Islamabad 45320, Pakistan
[2] Quaid i Azam Univ, ICESCO Chair Big Data Analyt & Edge Comp, Islamabad 45320, Pakistan
[3] Najran Univ, Coll Comp Sci & Informat Syst, Dept Comp Sci, Najran 61441, Saudi Arabia
[4] Edinburgh Napier Univ, Sch Comp Engn & Built Environm, Edinburgh EH10 5DT, Scotland
关键词
INTRUSION DETECTION;
D O I
10.3934/mbe.2023615
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:13824 / 13848
页数:25
相关论文
共 69 条
  • [21] High Performance Network Intrusion Detection System Using Two-Stage LSTM and Incremental Created Hybrid Features
    Han, Jonghoo
    Pak, Wooguil
    [J]. ELECTRONICS, 2023, 12 (04)
  • [22] Vehicle energy system active defense: A health assessment of lithium-ion batteries
    Hong, Sheng
    Yue, Tianyu
    Liu, Hao
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (12) : 10081 - 10099
  • [23] Cascading failure and recovery of spatially interdependent networks
    Hong, Sheng
    Zhu, Juxing
    Braunstein, Lidia A.
    Zhao, Tingdi
    You, Qiuju
    [J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2017,
  • [24] Cascading failure analysis and restoration strategy in an interdependent network
    Hong, Sheng
    Lv, Chuan
    Zhao, Tingdi
    Wang, Baoqing
    Wang, Jianghui
    Zhu, Juxing
    [J]. JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2016, 49 (19)
  • [25] Securing the operations in SCADA-IoT platform based industrial control system using ensemble of deep belief networks
    Huda, Shamsul
    Yearwood, John
    Hassan, Mohammad Mehedi
    Almogren, Ahmad
    [J]. APPLIED SOFT COMPUTING, 2018, 71 : 66 - 77
  • [26] Network intrusion detection based on IE-DBN model
    Jia, Huaping
    Liu, Jun
    Zhang, Min
    He, Xiaohu
    Sun, Weixi
    [J]. COMPUTER COMMUNICATIONS, 2021, 178 : 131 - 140
  • [27] Network intrusion detection algorithm based on deep neural network
    Jia, Yang
    Wang, Meng
    Wang, Yagang
    [J]. IET INFORMATION SECURITY, 2019, 13 (01) : 48 - 53
  • [28] Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology
    Jiao, Jian
    Zheng, Xue-Jiao
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [29] De Teyou GK, 2019, Arxiv, DOI arXiv:1905.03168
  • [30] Kaur M.J., 2019, DIGITAL TWIN TECHNOL, P3, DOI [DOI 10.1007/978-3-030-18732-3_1, 10.1007/978-3-030-18732-31, 10.1007/978-3-030-18732-3_1]