Abstract
As the adoption of Internet of Things (IoT) devices increases rapidly, industrial applications of IoT devices gain further popularity. Some of these applications, such as smart grids, are considered high-risk applications. In the past few years, smart grids became the target of many cyber attacks. In this paper, we present a two-stage system for the detection and classification of cyber attacks based on machine learning. The first stage of the proposed system focuses on detecting attacks efficiently and accurately. The second stage analyzes available data and predicts the specific attack class. The proposed system was tested using the DNP3 intrusion detection dataset, and delivered an F1 score of 0.9976 at the detection stage, and 0.9883 at the attack type classification stage.
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