A Predictive Model to Predict a Cyberattack Using Self Normalizing Neural Networks

Abstract

A cyberattack is an unauthorized access and a threat to information systems. Intelligent intrusion systems rely on advancements in technology to detect cyberattacks. In this article, the KDD CUP 99 dataset, from the Third International Knowledge Discovery and Data mining Tools Competition that was held in 1999, is considered, and a class of neural networks, known as Self-Normalizing Neural Networks, is utilized to build a predictive model to detect cyberattacks in the KDD CUP 99 dataset. The accuracy and the precision of the self-normalizing neural network is compared with that of the k-nearest neighbors and the support vector machines, in addition to other models in literature. The self-normalizing neural network appears to perform better than other models in predicting cyberattacks, while also being efficient in predicting a normal connection.