Anomaly Detection for Smart Lighting Infrastructure with the Use of Time Series Analysis

• Main Authors: Tomasz Andrysiak, Łukasz Saganowski
• Format: Article
• Language: English
• Published: Graz University of Technology 2020-04-01
• Series: Journal of Universal Computer Science
• Subjects: anomaly detection; time series analysis; outliers
• Online Access: https://lib.jucs.org/article/24012/download/pdf/

One of the basic elements of every Smart City is currently a system of managing urban infrastructure, in particular, smart systems controlling street lighting. Ensuring proper level of security, continuity and failure-free operation of such systems, in practice, seems not yet a solved problem. In this article we present proposals of a system allowing to detect different types of anomalies in network traffic for Smart Lighting critical infrastructure realized with the use of Power Line Communication technology. Furthermore, there is proposed and described the structure of the examined Smart Lighting Communications Network along with its particular elements. We discuss key security aspects which affect proper operation of advance communication infrastructure, i.e. possibility of occurrence of abuse connected both to activity of external factors which could disturb transmission of steering signals, as well as active forms of attack aiming at influencing the informative content of the transmitted data. In the article, there is also presented an effective and quick anomaly detection method in the tested network traffic represented by suitable time series. At the initial stage of the method, the process of detection and elimination of potential outlying observations was realized by one-dimensional quartile criterion. Data prepared in this manner was used for learning recurrent neural networks, i.e. Long and Short-Term Memory types, in order to predict values of the analyzed time series. Further, tests were performed on relations between the forecasted network traffic and its real variability in order to detect abnormal behavior which could mean an attempt of an attack or abuse. Due to a possibility of occurrence of significant fluctuations in real network traffic of the tested Smart Lighting infrastructure, we propose a procedure of recurrent learning with the use of neural networks to obtain more accurate forecasting. The results achieved by means of the performed experiments confirmed effectiveness of the presented method and proper choice of the Long Short-Term Memory neural network for forecasting the analyzed time series.