An Evaluation of Machine Learning Algorithms To Detect Attacks in Scada Network

Tamy, Sara; Belhadaoui, Hicham; Rabbah, Mahmoud Almostafa; Rabbah, Nabila; Rifi, Mounir

doi:10.1109/cmt.2019.8931327

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…have been proposed in several intrusion prediction, detection and classification of SCADA datasets and other problems that needed to be addressed. In some of the reviewed articles, while some authors proposed singular supervised learning models [32,57], several authors [81][82][83][84] ensemble two or more models, with the aim of achieving improved performances. Moreover, some authors compared several supervised learning models on specific dataset(s), in order to establish the best possible model for the analyzed SCADA system and testbed.…”

Section: Classification Mechanismmentioning

confidence: 99%

“…Developed by Breiman, RF is an efficient supervised learning algorithm that is widely used in various classification studies [81]. RF is an ensemble method that are constituted by a set of tree structured classifiers.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…RFs operate by training a couple of DTs and they return the class with the majority over all the trees in the ensemble [109]. Tamy et al [81] explained that DTs are combinations of sets of tree predictors whereby individual tree depends on the values of a random vector that is sampled independently with the same distribution for the trees that makes up the forest. Rakhra et al [110] explained that RFs are collections of multiple DTs whereby the challenge of overfitting which is popular with singular DTs is solved by a voting method, in which the most voted class is the final result for the target observation.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…From the study, RF and NNge presented the best results. Tamy et al [81] also did a comparative study involving RF, Naïve Bayes, SVM, and J48 Tree using MSU gas pipeline dataset. The authors in [99] also did a comparison work involving two supervised learning algorithms; RF and SVM for the classification of two SCADA datasets.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

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A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

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Section: Classification Mechanismmentioning

confidence: 99%

Section: Random Forest (Rf)mentioning

confidence: 99%

Section: Random Forest (Rf)mentioning

confidence: 99%

Section: Random Forest (Rf)mentioning

confidence: 99%

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A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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“…Today, industries usually use supervised machine learning techniques for intelligent industrial monitoring. Supervised learning methods such as the Na've Bayes [7], The Support Vector Machine [8], etc. can be used to implement data classification and regression, but only after the phase of automatic feature creation.…”

Section: B Process-independent Smart Monitoring Algorithmsmentioning

confidence: 99%

SMILE: Smart Monitoring IoT Learning Ecosystem

Avanzato

Beritelli

Franco³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results. Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.

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