Abstract—In this paper, we propose a novel method using ensemble learning scheme for classifying network intrusion detection from the most renowned KDD cup dataset. We have shown that reducing the dimensionality of the large dataset provides most accurate detection. Additionally, several machine learning algorithms are used to generate the accuracy metrics and analyzed further for proper comparison. Our approach found out that this algorithm outperforms all other learning techniques. Our goal is to analyze the network intrusion data and find out the best components and use them for the attack analysis. This scheme can be used in parallel with the intrusion detection system to augment its prediction performance for the future data packets. Empirical results show that the input dimensionality reduction can provide lightweight intrusion detection system that can be embedded with the vulnerable system for generating correct classification with significance improvement in execution time.
Index Terms—Network intrusion detection, Random Forest, PART, Naive Bayes, machine learning.
Md. Enamul Haque is with King Fahd University of Petroleum & Minerals, Dhahran, 31261, Kingdom of Saudi Arabia (tel.: +966-50-2389368; e-mail: email@example.com, firstname.lastname@example.org).
Talal M. Alkharobi is with the Department of Computer Engineering, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Kingdom of Saudi Arabia.
Cite: Md. Enamul Haque and Talal M. Alkharobi, "Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms," International Journal of Machine Learning and Computing vol. 5, no. 1, pp. 17-23, 2015.