Improving the Attack Detection Rate in Network Intrusion Detection using Adaboost Algorithm

Abstract

Problem statement: Nowadays, the Internet plays an important role in communication between people. To ensure a secure communication between two parties, we need a security system to detect the attacks very effectively. Network intrusion detection serves as a major system to work with other security system to protect the computer networks. Approach: In this article, an Adaboost algorithm for network intrusion detection system with single weak classifier is proposed. The classifiers such as Bayes Net, Naive Bayes and Decision tree are used as weak classifiers. A benchmark data set is used in these experiments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak classification algorithms. Results: Our approach achieves a higher detection rate with low false alarm rates and is scalable for large data sets, resulting in an effective intrusion detection system. Conclusion: The Naive Bayes and Decision Tree Classifiers have comparatively better performance as a weak classifier with Adaboost, it should be considered for the building of IDS.