Intrusion Detection and Anomalies in Sensory Data Using Random Forest Algorithm

Abstract

Today, security in computer networks is of great importance. However, since industrial networks have their own distinctions and needs compared to conventional networks, these differences prioritize and secure methods in industrial networks over ordinary networks. SCADA systems are an integral part of large industrial systems that collect information and control equipment. Given the lack of an integrated definition of security in industrial systems, it may not be a good solution to ensure absolute security of the system, but by applying security assessment strategies, vulnerabilities in industrial control systems can be identified and reduced or eliminated as far as possible. This study aimed to detect anomalies in SCADA systems. Due to the lack of access to real anomalous data on these networks, artificial data was created. This process was accomplished by implementing an artificial malformation-generating algorithm in Java programming language. This process was performed for the ARIMA model in R software and for the random forest algorithm in R in the Scala programming language on the big data platform. In this study, it was attempted to generate anomalous SCADA data by implementing a distribution-based artificial anomaly generation algorithm and then to perform anomaly detection and anomaly process using random forest algorithms and bulk data on SCADA systems.