Application of Explainable Artificial Intelligence in Intrusion Detection System Classification Using Boosting Algorithms

Abstract

The increased speed rates and ease of access to the Internet increase the availability of devices with Internet connections. Internet users can access many devices that they are authorized or not authorized. These systems, which detect whether users have unauthorized access or not, are called Intrusion Detection Systems. With intrusion detection systems, users' access is classified and it is determined whether it is a normal login or an anomaly. Machine learning methods undertake this classification task. In particular, Boosting algorithms stand out with their high classification performance. It has been observed that the Gradient Boosting algorithm provides remarkable classification performance when compared to other methods proposed for the Intrusion Detection Systems problem. Using the Python programming language, estimation was made with the Gradient Boost, Adaboost algorithms, Catboost, and Decision Tree and then the model was explained with SHAPASH. The goal of SHAPASH is to enable universal interpretation and comprehension of machine learning models. Providing an interpretable and explainable approach to Intrusion Detection Systems contributes to taking important precautions in the field of cyber security. In this study, classification was made using Boosting algorithms, and the estimation model created with SHAPASH, which is one of the Explainable Artificial Intelligence approaches, is explained.