Extreme Gradient Boosting Based Tuning for Classification in Intrusion Detection Systems

In a fast-growing digital era, the increase in devices connected to internet have raised many security issues. For providing security, varieties of the system are available in the IT sector, Intrusion Detection system is one of such system. The design of an efficient intrusion detection system is an open problem to the research community. In this paper, various machine learning algorithms have been used for detecting different types of Denial-of-Service attack. The performance of the models have been measured on the basis of binary and multi-classification. Furthermore, parameter tuning algorithm has been discussed. On the basis of performance parameters, XGBoost performs efficiently and in robust manner to find an intrusion. The proposed method i.e. XGBoost has been compared with other classifiers like AdaBoost, Naive Bayes, Multi-layer perceptron (MLP) and K-Nearest Neighbour (KNN) on recently captured network traffic by Canadian Institute of Cybersecurity (CIC). In this research, average class error and overall error have been calculated for the multi-classification problem.