  Network Control Systems (NAC) have been used in many industrial processes.
They aim to reduce the human factor burden and efficiently handle the complex
process and communication of those systems. Supervisory control and data
acquisition (SCADA) systems are used in industrial, infrastructure and facility
processes (e.g. manufacturing, fabrication, oil and water pipelines, building
ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA
systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection
is a major requirement. However, having an accurate anomaly detection system is
not an easy task, due to the difficulty to differentiate between cyber-attacks
and system internal failures (e.g. hardware failures). In this paper, we
present a model that detects anomaly events in a water system controlled by
SCADA. Six Machine Learning techniques have been used in building and
evaluating the model. The model classifies different anomaly events including
hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS
and Spoofing). Unlike other detection systems, our proposed work focuses on
notifying the operator when an anomaly occurs with a probability of the event
occurring. This additional information helps in accelerating the mitigation
process. The model is trained and tested using a real-world dataset.
