Privacy Preservation Intrusion Detection Technique for SCADA Systems

TL;DR

This paper introduces a novel privacy-preserving intrusion detection method for SCADA systems that combines correlation coefficient analysis and EM clustering to improve detection accuracy and data privacy.

Contribution

The paper proposes a new PPID technique using correlation coefficient and EM clustering, enhancing intrusion detection and privacy preservation in SCADA systems.

Findings

Outperforms three existing techniques in detection accuracy

Effective in identifying multiclass attacks in power system datasets

Demonstrates high reliability and efficiency for SCADA security

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.