All accepted publications from SPARTA partners under its funding.
The proposition of balanced and explainable surrogate method for network intrusion detection in streamed real difficult data
Mateusz Szczepanski, Mikołaj Komisarek, Marek Pawlicki,Rafał KozikMichał ChoraśAbstract
Handling the data imbalance problem is one of the crucial steps in a machine learning pipeline. The research community is well aware of the effects of data imbalance on machine learning algorithms. At the same time, there is a rising need for explainability of AI, especially in difficult, high-stake domains like network intrusion detection. In this paper, the effects of data balancing procedures on two explainability procedures implemented to explain a neural network used for network intrusion detection are evaluated. The discrepancies between the two methods are highlighted and important conclusions are drawn.