On Using Physical Based Intrusion Detection in SCADA Systems

被引:12
|
作者
Al-Asiri, Majed [1 ]
El-Alfy, El-Sayed M. [1 ]
机构
[1] King Fahd Univ Petr & Minerals, Dhahran 31261, Saudi Arabia
关键词
Information Security; SCADA; Industrial Control Systems; Cyber Physical Systems (CPS); Industrial Internet of Things (IIoT); Intrusion Detection; Taxonomy; SECURITY;
D O I
10.1016/j.procs.2020.03.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Intrusion detection in SCADA systems has received increased attention from researchers as connectivity to public networks became a necessity in many industries. The nature and characteristics of SCADA systems call for special considerations and techniques of intrusion detection. Many works have been made in this field, ranging from generic intrusion detection techniques to customized solutions designed specifically for SCADA systems. In the recent years, some works have focused on using physical metrics in addition to the popular network-based and host-based intrusion detection approaches. This paper presents a taxonomy that considers the special features of cyberphysical intrusion detection systems (IDSs) with examples from the literature. Moreover, a case study is presented for a simulated gas pipeline dataset to compare the effectiveness of decision tree classifiers for various categories of features in SCADA systems. The results show that an IDS that uses a combination of physical and network metrics significantly outperforms an IDS that only uses network metrics or physical metrics. (C) 2020 The Authors. Published by Elsevier B.V.
引用
收藏
页码:34 / 42
页数:9
相关论文
共 50 条
  • [1] Supervised learning based intrusion detection for SCADA systems
    Alimi, Oyeniyi Akeem
    Ouahada, Khmaies
    Abu-Mahfouz, Adnan M.
    Rimer, Suvendi
    Alimi, Kuburat Oyeranti Adefemi
    2022 IEEE NIGERIA 4TH INTERNATIONAL CONFERENCE ON DISRUPTIVE TECHNOLOGIES FOR SUSTAINABLE DEVELOPMENT (IEEE NIGERCON), 2022, : 141 - 145
  • [2] Intrusion Detection in SCADA systems using Machine Learning Techniques
    Maglaras, Leandros A.
    Jiang, Jianmin
    2014 SCIENCE AND INFORMATION CONFERENCE (SAI), 2014, : 626 - 631
  • [3] A Transfer Function based Intrusion Detection System for SCADA Systems
    Papa, Stephen
    Casper, William
    Nair, Suku
    2012 IEEE INTERNATIONAL CONFERENCE ON TECHNOLOGIES FOR HOMELAND SECURITY, 2012, : 93 - 98
  • [4] INTRUSION DETECTION IN SCADA SYSTEMS USING ONE-CLASS CLASSIFICATION
    Nader, Patric
    Honeine, Paul
    Beauseroy, Pierre
    2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2013,
  • [5] Machine learning-based intrusion detection for SCADA systems in healthcare
    Ozturk, Tolgahan
    Turgut, Zeynep
    Akgun, Gokce
    Kose, Cemal
    NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, 2022, 11 (01):
  • [6] Probability Risk Identification Based Intrusion Detection System for SCADA Systems
    Marsden, Thomas
    Moustafa, Nour
    Sitnikova, Elena
    Creech, Gideon
    MOBILE NETWORKS AND MANAGEMENT (MONAMI 2017), 2018, 235 : 353 - 363
  • [7] Machine learning-based intrusion detection for SCADA systems in healthcare
    Öztürk, Tolgahan
    Turgut, Zeynep
    Akgün, Gökçe
    Köse, Cemal
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2022, 11 (01)
  • [8] Machine learning-based intrusion detection for SCADA systems in healthcare
    Tolgahan Öztürk
    Zeynep Turgut
    Gökçe Akgün
    Cemal Köse
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2022, 11
  • [9] Deep-Learning-Based Network Intrusion Detection for SCADA Systems
    Yang, Huan
    Cheng, Liang
    Chuah, Mooi Choo
    2019 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS), 2019,
  • [10] SCO-RNN: A Behavioral-Based Intrusion Detection Approach for Cyber Physical Attacks in SCADA Systems
    Neha, N.
    Priyanga, S.
    Seshan, Suresh
    Senthilnathan, R.
    Sriram, V. S. Shankar
    INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES, ICICCT 2019, 2020, 89 : 911 - 919