Rare Sequential Pattern Mining of Critical Infrastructure Control Logs for Anomaly Detection
Anisur Rahman · Queensland University of Technology · 2019
Supervisory Control and Data Acquisition (SCADA) systems are used to drive much of a nation's critical infrastructure, which by definition is essential for the nation's citizens' way of life. They are connected to the computer networks and internet systems to operate, control and monitor their operations. This connectivity enables these SCADA systems to be exposed to cyber-attacks. This thesis detects anomalies or cyber-attacks on SCADA systems. It analyses SCADA control logs to find abnormal process activities which are treated as anomalies. A novel rare sequential pattern mining approach is proposed and developed to find rare or abnormal behaviour in SCADA systems.