Advantages of a neuro-symbolic solution for monitoring IT infrastructures alerts

Darian M. Onchis, Codruta Istin, Eduard Hogea

Abstract: The classification and at the same time the inter-active characterization of both bad connections, called alerts or attacks, as well as normal connections, is a must for monitoring network traffic. For this specific task, we developed in this study a neuro-symbolic predictive model based on Logic Tensor Networks. Moreover, we present in detail the advantages and disadvantages of using our hybrid system versus the usage of a standard feed-forward deep neural network classifier. For a relevant comparison, the same dataset was used during training and the metrics resulted have been compared. An overview shows that while both algorithms have similar precision, the hybrid approach gives also the possibility to have interactive explanations and deductive reasoning over data.