Publication Details
Statistical Methods for Anomaly Detection in Industrial Communication
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT)
Mutua Nelson Makau, MSc. (DIFS FIT BUT)
anomaly detection, communication patterns, industrial networks, IEC 104, monitoring
This report focuses on application of selected statistical methods to anomaly detection of ICS protocols deployed in smart grids, namely IEC 104, GOOSE and MMS. Industrial network stations are typically pre-configured hardware devices that operate in master-slave mode and exhibits stable and periodic communication patterns over a long time. Due to the stability of ICS communication, statistical models present a natural way for detection of common ICS anomalies.
For probabilistic modeling of network behavior we employ the following statistical features: distribution of packet inter-arrival times, packet size, and packet direction. This report presents the results of our experiments with three statistical methods: the Box Plot, Three Sigma Rule and Local Outlier Factor (LOF) which worked best for ICS datasets.
@TECHREPORT{FITPUB12502,
author = "Ivana Burgetov\'{a} and Petr Matou\v{s}ek and Makau Nelson Mutua",
title = "Statistical Methods for Anomaly Detection in Industrial Communication",
pages = 59,
year = 2021,
location = "IT-TR-2021-01, Brno, CZ",
publisher = "Faculty of Information Technology BUT",
language = "english",
url = "https://www.fit.vut.cz/research/publication/12502"
}