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      Privacy Preservation Intrusion Detection Technique for SCADA Systems

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          Abstract

          Supervisory Control and Data Acquisition (SCADA) systems face the absence of a protection technique that can beat different types of intrusions and protect the data from disclosure while handling this data using other applications, specifically Intrusion Detection System (IDS). The SCADA system can manage the critical infrastructure of industrial control environments. Protecting sensitive information is a difficult task to achieve in reality with the connection of physical and digital systems. Hence, privacy preservation techniques have become effective in order to protect sensitive/private information and to detect malicious activities, but they are not accurate in terms of error detection, sensitivity percentage of data disclosure. In this paper, we propose a new Privacy Preservation Intrusion Detection (PPID) technique based on the correlation coefficient and Expectation Maximisation (EM) clustering mechanisms for selecting important portions of data and recognizing intrusive events. This technique is evaluated on the power system datasets for multiclass attacks to measure its reliability for detecting suspicious activities. The experimental results outperform three techniques in the above terms, showing the efficiency and effectiveness of the proposed technique to be utilized for current SCADA systems.

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          Most cited references 9

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          Introduction to Industrial Control Networks

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            The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set

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              Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues

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                Author and article information

                Journal
                07 November 2017
                Article
                1711.02828

                http://creativecommons.org/publicdomain/zero/1.0/

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                cs.CR

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