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Filters: Author is Vávra, J.  [Clear All Filters]
Conference Paper
Vávra, J., Hromada, M..  2017.  Anomaly Detection System Based on Classifier Fusion in ICS Environment. 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT). :32–38.

The detection of cyber-attacks has become a crucial task for highly sophisticated systems like industrial control systems (ICS). These systems are an essential part of critical information infrastructure. Therefore, we can highlight their vital role in contemporary society. The effective and reliable ICS cyber defense is a significant challenge for the cyber security community. Thus, intrusion detection is one of the demanding tasks for the cyber security researchers. In this article, we examine classification problem. The proposed detection system is based on supervised anomaly detection techniques. Moreover, we utilized classifiers algorithms in order to increase intrusion detection capabilities. The fusion of the classifiers is the way how to achieve the predefined goal.