Int. J. Internet Technology and Secured Transactions, Vol. 8, No. 4, 2018 635 Copyright © 2018 Inderscience Enterprises Ltd. Intrusion detection model using feature extraction and LPBoost technique I. Sumaiya Thaseen* and Ch. Aswani Kumar School of Information Technology and Engineering, VIT University, Vellore, 632014, India Email: sumaiyathaseen@gmail.com Email: aswanis@gmail.com *Corresponding author Abstract: A lot of intrusion and hacking events are surrounding the internet domain, bringing in a need for security systems. Intrusion detection system (IDS) is employed in the network to identify the attacks by continuously monitoring the system activities. The major issue for any intrusion detection model is to identify anomalies with maximum accuracy and minimal false alarms. An intrusion detection model is developed combining chi-square feature selection and LPBoost algorithm. Chi-square feature selection is deployed to build the optimal features as the network traffic data consists of many attributes. The optimum features are utilised by the LPBoost algorithm for classification of network traffic. An ensemble classifier is chosen as it typically outperforms a single classifier. The experiments are performed using NSL-KDD and UNSW-NB data sets. The results clearly show that the hybrid model achieves a higher detection and reduced false positive rate in contrast to other techniques. Keywords: boosting; chi-square; ensemble; feature selection; intrusion detection; linear programming. Reference to this paper should be made as follows: Thaseen, I.S. and Kumar, C.A. (2018) ‘Intrusion detection model using feature extraction and LPBoost technique’, Int. J. Internet Technology and Secured Transactions, Vol. 8, No. 4, pp.635–652. Biographical notes: I. Sumaiya Thaseen is an Assistant Professor (selection grade) in VIT University, Vellore with nearly 11 years of experience and completed PhD in the area of intrusion detection using pattern recognition techniques. She has published many papers in reputed journals and conferences. Her research interests include intrusion detection, machine learning, cryptography and network security. Ch. Aswani Kumar is a Professor in School of Information Technology and Engineering, VIT University, Vellore, India. He holds a PhD degree in Computer Science from VIT University, India. He also possesses Bachelor’s and Master’s degrees in Computer Science from Nagarjuna University, India. His current research interests are big data, information security, and machine intelligence. He has published more than 100 refereed research papers so far in various national, international journals and conferences. He is a senior member of ACM and is a reviewer for many reputed international journals and conferences.