A Pattern Mining-Based False Data Injection Attack Detector for Industrial Cyber-Physical Systems
Khalil Guibene, Nadhir Messai, Marwane Ayaida, Lyes Khoukhi
Abstract
The implication of cyber-physical systems into industrial processes has introduced some security breaches due to the lack of security mechanisms. This article aims to come up with a novel methodology to detect false data injection attacks on cyber-physical systems. To reach this goal, we propose an efficient anomaly-based approach for detecting false data injection attacks against industrial cyber-physical systems. Particularly, we use sequential pattern mining techniques, which are commonly used for learning most important patterns of a system. In our case, the frequent pattern learning algorithm is used to create a database corresponding to the normal operation of the system, then, this database is fed into an attack detection algorithm in order to alert the user whenever an attack is occurring. The extensive simulations prove that our attack detection approach is able to detect attacks with a great accuracy and that this methodology could work even for large scale systems.