A Semi-Supervised Approach for Detection of SCADA Attacks in Gas Pipeline Control Systems

Joshi, Chaitali; Khochare, Janavi; Rathod, Jash; Kazi, Faruk

doi:10.1109/hydcon48903.2020.9242676

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Results showed the outperformance of this ensemble model compared to the standalone FNN and LSTM-based IDSs. A semi-supervised deep learning autoencoder is employed in [39] to improve the detection of SCADA attacks in gas pipeline control systems. Specifically, this scheme learns the most relevant features based on attacks-free data; thus, malicious data could be easily flagged out because it leads to a high reconstruction error.…”

Section: Related Workmentioning

confidence: 99%

A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems

Harrou

Bouyeddou

et al. 2021

Cluster Comput

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Presently, Supervisory Control and Data Acquisition (SCADA) systems are broadly adopted in remote monitoring large-scale production systems and modern power grids. However, SCADA systems are continuously exposed to various heterogeneous cyberattacks, making the detection task using the conventional intrusion detection systems (IDSs) very challenging. Furthermore, conventional security solutions, such as firewalls, and antivirus software, are not appropriate for fully protecting SCADA systems because they have distinct specifications. Thus, accurately detecting cyber-attacks in critical SCADA systems is undoubtedly indispensable to enhance their resilience, ensure safe operations, and avoid costly maintenance. The overarching goal of this paper is to detect malicious intrusions that already detoured traditional IDS and firewalls. In this paper, a stacked deep learning method is introduced to identify malicious attacks targeting SCADA systems. Specifically, we investigate the feasibility of a deep learning approach for intrusion detection in SCADA systems. Real data sets from two laboratory-scale SCADA systems, a two-line three-bus power transmission system and a gas pipeline are used to evaluate the proposed method’s performance. The results of this investigation show the satisfying detection performance of the proposed stacked deep learning approach. This study also showed that the proposed approach outperformed the standalone deep learning models and the state-of-the-art algorithms, including Nearest neighbor, Random forests, Naive Bayes, Adaboost, Support Vector Machine, and oneR. Besides detecting the malicious attacks, we also investigate the feature importance of the cyber-attacks detection process using the Random Forest procedure, which helps design more parsimonious models.

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Section: Related Workmentioning

confidence: 99%

A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems

Harrou

Bouyeddou

et al. 2021

Cluster Comput

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Incident Detection System for Industrial Networks

Kuchař

Holasova

Fujdiak

et al. 2022

Advanced Sciences and Technologies for Security Applications

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A Systematic Mapping Study and Empirical Comparison of Data-Driven Intrusion Detection Techniques in Industrial Control Networks

Tama

Lee

2022

Arch Computat Methods Eng

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A rising communication between modern industrial control infrastructure and the external Internet worldwide has led to a critical need to secure the network from multifarious cyberattacks. An intrusion detection system (IDS) is a preventive mechanism where new sorts of hazardous threats and malicious activities could be detected before harming the industrial process’s critical infrastructure. This study reviews the cutting-edge technology of artificial intelligence in developing IDS in industrial control networks by carrying out a systematic mapping study. We included 74 foremost publications from the current literature. These chosen publications were grouped following the types of learning tasks, i.e., supervised, unsupervised, and semi-supervised. This review article helps researchers understand the present status of artificial intelligence techniques applied to IDS in industrial control networks. Other mapping categories were also covered, including year published, publication venues, dataset considered, and IDS approaches. This study reports an empirical assessment of several classification algorithms such as random forest, gradient boosting machine, extreme gradient boosting machine, deep neural network, and stacked generalization ensemble. Statistical significance tests were also used to assess the classifiers’ performance differences in multiple scenarios and datasets. This paper provides a contemporary systematic mapping study and empirical evaluation of IDS approaches in industrial control networks.

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A Semi-Supervised Approach for Detection of SCADA Attacks in Gas Pipeline Control Systems

Cited by 4 publications

References 28 publications

A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems

A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems

Incident Detection System for Industrial Networks

A Systematic Mapping Study and Empirical Comparison of Data-Driven Intrusion Detection Techniques in Industrial Control Networks

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