A nested decision tree for event detection in smart grids

Digitalization process experienced by traditional power networks towards smart grids extend the challenges faced grid operators to field of cybersecurity. False data injection attacks, one most common cyberattacks in grids, could lead sabotage itself. In this paper, an event detection algorithm for cyberattack is developed based on a decision tree. order find accurate algorithm, two different trees with goals have been trained: classifies status network, corresponding event, and other will classify location where detected. To train trees, dataset made co-simulating network communication has used. The are going be compared settings changing division criteria, used them misclassification cost. After looking at their performance independently, best way combine into single presented.