Intrusion Detection Based on Sequential Information Preserving Log Embedding Methods and Anomaly Det

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.