Abstract
Though the problem of anomaly detection in a cyber-physical object (CPO) behaviour is highly researched topic, there are still open challenges in this field that mainly relate to the lack of annotated real world data sets. Manual analysis of the anomalies and incidents in such data sets poses a certain difficulties as a typical cyber physical object is described by hundreds of heterogeneous parameters. This paper proposes an approach targeted to simplify the process of anomaly detection and investigation in CPO behaviour represented by multivariate time series. It is based on a novel metric that reflects a measure of the changes in CPO’s behaviour. This metric incorporates data from a set of parameters defined by an analyst, and outputs a high level integral value that could be easily visualized using a standard timeline. The efficiency of the approach is demonstrated on real world data set describing the behaviour of aerospace object and consisting of almost 500 attributes.
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Shulepov, A., Novikova, E., Murenin, I. (2022). Approach to Anomaly Detection in Cyber-Physical Object Behavior. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_38
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