Intrusion Detection of SCADA System Using Machine Learning Techniques

Chakrabarti, Koyela

doi:10.1201/9781003054115-7

Cited by 1 publication

(1 citation statement)

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“…In the recent days, the Supervisory Control and Data Acquisition (SCADA) [1,2] is one of the widely used control systems in the industrial sectors like traffic maintenance, electric power generation, nuclear systems, water/waste water treatment oil mining, and space stations. It is highly important to secure the SCADA systems against the attacking activities of malfunctioning and threatening [3][4][5]. The present SCADA systems comprise certain security features (such as user interfaces, communication, and control logic), which helps to increase the system complexity, but maintaining those features is one the critical task [6].…”

Section: Introductionmentioning

confidence: 99%

An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems

Shitharth¹,

Prasad

Sangeetha³

et al. 2021

IEEE Access

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Providing security to the Supervisory Control and Data Acquisition (SCADA) systems is one of the demanding and crucial tasks in recent days, due to the different types of attacks on the network. For this purpose, there are different types of attack detection and classification methodologies have been developed in the conventional works. But it limits with the issues like high complexity in design, misclassification results, increased error rate, and reduced detection efficiency. In order to solve these issues, this paper aims to develop an advanced machine learning models for improving the SCADA security. This work comprises the stages of preprocessing, clustering, feature selection, and classification. At first, the Markov Chain Clustering (MCC) model is implemented to cluster the network data by normalizing the feature values. Then, the Rapid Probabilistic Correlated Optimization (RPCO) mechanism is employed to select the optimal features by computing the matching score and likelihood of particles. Finally, the Block Correlated Neural Network (BCNN) technique is employed to classify the predicted label, where the relevancy score is computed by using the kernel function with the feature points. During experimentation, there are different performance indicators have been used to validate the results of proposed attack detection mechanisms. Also, the obtained results are compared with the RPCO-BCNN mechanism for proving the superiority of the proposed attack detection system.

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