  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.
