MADICS: A Methodology for Anomaly Detection in Industrial Control Systems

被引:44
作者
Perales Gomez, Angel Luis [1 ]
Fernandez Maimo, Lorenzo [1 ]
Huertas Celdran, Alberto [2 ]
Garcia Clemente, Felix J. [1 ]
机构
[1] Univ Murcia, Dept Ingn & Tecnol Comp, Murcia 30100, Spain
[2] Waterford Inst Technol, Telecommun Software & Syst Grp, Waterford X91 P20H, Ireland
来源
SYMMETRY-BASEL | 2020年 / 12卷 / 10期
关键词
anomaly detection; artificial intelligence; critical infrastructures; deep learning; industrial control systems; industry applications; machine learning;
D O I
10.3390/sym12101583
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Industrial Control Systems (ICSs) are widely used in critical infrastructures to support the essential services of society. Therefore, their protection against terrorist activities, natural disasters, and cyber threats is critical. Diverse cyber attack detection systems have been proposed over the years, in which each proposal has applied different steps and methods. However, there is a significant gap in the literature regarding methodologies to detect cyber attacks in ICS scenarios. The lack of such methodologies prevents researchers from being able to accurately compare proposals and results. In this work, we present a Methodology for Anomaly Detection in Industrial Control Systems (MADICS) to detect cyber attacks in ICS scenarios, which is intended to provide a guideline for future works in the field. MADICS is based on a semi-supervised anomaly detection paradigm and makes use of deep learning algorithms to model ICS behaviors. It consists of five main steps, focused on pre-processing the dataset to be used with the machine learning and deep learning algorithms; performing feature filtering to remove those features that do not meet the requirements; feature extraction processes to obtain higher order features; selecting, fine-tuning, and training the most appropriate model; and validating the model performance. In order to validate MADICS, we used the popular Secure Water Treatment (SWaT) dataset, which was collected from a fully operational water treatment plant. The experiments demonstrate that, using MADICS, we can achieve a state-of-the-art precision of 0.984 (as well as a recall of 0.750 and F1-score of 0.851), which is above the average of other works, proving that the proposed methodology is suitable for use in real ICS scenarios.
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页数:23
相关论文
共 46 条
[1]   Network anomaly detection using Two-dimensional Hidden Markov Model-based Viterbi algorithm [J].
Alhaidari, Sulaiman ;
Zohdy, Mohamed .
2019 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE TESTING (AITEST), 2019, :17-18
[2]  
[Anonymous], arXiv
[3]   Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series [J].
Ceschini, Giuseppe Fabio ;
Gatta, Nicolo ;
Venturini, Mauro ;
Hubauer, Thomas ;
Murarasu, Alin .
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, 2018, 140 (03)
[4]  
Chollet F., 2015, Keras
[5]   A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems [J].
Elnour, Mariam ;
Meskin, Nader ;
Khan, Khaled ;
Jain, Raj .
IEEE ACCESS, 2020, 8 :36639-36651
[6]   Graphene with outstanding anti-irradiation capacity as multialkylated cyclopentanes additive toward space application [J].
Fan, Xiaoqiang ;
Wang, Liping .
SCIENTIFIC REPORTS, 2015, 5
[7]   Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments [J].
Fernandez Maimo, Lorenzo ;
Huertas Celdran, Alberto ;
Perales Gomez, Angel L. ;
Garcia Clemente, Felix J. ;
Weimer, James ;
Lee, Insup .
SENSORS, 2019, 19 (05)
[8]   Dynamic management of a deep learning-based anomaly detection system for 5G networks [J].
Fernandez Maimo, Lorenzo ;
Huertas Celdran, Alberto ;
Gil Perez, Manuel ;
Garcia Clemente, Felix J. ;
Martinez Perez, Gregorio .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (08) :3083-3097
[9]   A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks [J].
Fernandez Maimo, Lorenzo ;
Perales Gomez, Angel Luis ;
Garcia Clemente, Felix J. ;
Gil Perez, Manuel ;
Martinez Perez, Gregorio .
IEEE ACCESS, 2018, 6 :7700-7712
[10]   A Dataset to Support Research in the Design of Secure Water Treatment Systems [J].
Goh, Jonathan ;
Adepu, Sridhar ;
Junejo, Khurum Nazir ;
Mathur, Aditya .
CRITICAL INFORMATION INFRASTRUCTURES SECURITY (CRITIS 2016), 2018, 10242 :88-99