Anomaly Detection System Based on Classifier Fusion in ICS Environment

Title	Anomaly Detection System Based on Classifier Fusion in ICS Environment
Publication Type	Conference Paper
Year of Publication	2017
Authors	Vávra, J., Hromada, M.
Conference Name	2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT)
Keywords	anomaly detection, anomaly detection system, Classification algorithms, classifier, classifier fusion, classifiers algorithms, computer security, critical information infrastructure, cyber security, cyber-attacks, Decision trees, demanding tasks, ICS Anomaly Detection, ICS cyber defense, ICS environment, industrial control, industrial control system, industrial control systems, integrated circuits, Intrusion detection, pattern classification, production engineering computing, pubcrawl, reliable ICS cyber defense, resilience, Resiliency, Scalability, security of data, sensor fusion, supervised anomaly detection, Support vector machines, Training
Abstract	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.
URL	https://www.computer.org/csdl/proceedings/icsiit/2017/9899/00/9899a032-abs.html
DOI	10.1109/ICSIIT.2017.35
Citation Key	vavra_anomaly_2017