Abstract. This paper will show how the accuracy and security of SCADA systems can be improved by using anomaly detection to identify bad values caused by attacks and faults. The performance of invariant induction and ngram anomaly-detectors will be compared and this paper will also outline plans for taking this work further by integrating the output from several anomalydetecting techniques using Bayesian networks. Although the methods outlined in this paper are illustrated using the data from an electricity network, this research springs from a more general attempt to improve the security and dependability of SCADA systems using anomaly detection.
Statistical Bayesian Networks were used in the work REF to improve the accuracy of anomaly detection for SCADA systems.
Safeguarding SCADA Systems with Anomaly Detection
Proceedings of the Second International Workshop on Mathematical Methods, Models and Architectures for Computer Network Security MMMACNS 2003, Lecture Notes in Computer Science
Computer Science