Platform Management System Host-Based Anomaly Detection using TF-IDF and an LSTM Autoencoder
Emilie Coote, Brian Lachine · 2023
Supervisory Control and Data Acquisition (SCADA) systems are at the core of many types of critical infrastructure and have become high value targets for cyber attack. SCADA systems are designed to be both available and reliable. With the identification of possible vectors for cyber attack there is a need for monitoring these systems for malicious behaviour. One type of SCADA network is a Platform Management System (PMS) that enables the centralized control and monitoring of numerous subsystems.The aim of this research is to determine the effectiveness of natural language processing and deep learning techniques in detecting host-based anomalies within a PMS network. Effectiveness is determined through the metrics derived from the confusion matrix. System monitor (Sysmon) logs are collected from the PMS subsystem hosts and features are extracted from these host logs using Term Frequency – Inverse Document Frequency (TF-IDF). A Long Short-Term Memory (LSTM) autoencoder is used to detect anomalies.In order to achieve this aim, a pipeline was developed for anomaly detection. Host logs were collected from subsystems on the PMS network and processed using TF-IDF, then used to train the LSTM model. Once trained, new attack data was introduced to the anomaly detection pipeline to determine the effectiveness of NLP and deep learning techniques to detect host-based anomalies within the PMS network.Performance metrics of this anomaly detection pipeline are presented, including accuracy, precision, recall, F1 Score and Matthew’s Correlation Coefficient (MCC). The results of this research show that the proposed pipeline can detect a range of cyber attacks occurring on the PMS network.