Intrusion detection model using feature extraction and LPBoost technique Online publication date: Mon, 05-Nov-2018
by I. Sumaiya Thaseen; Ch. Aswani Kumar
International Journal of Internet Technology and Secured Transactions (IJITST), Vol. 8, No. 4, 2018
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.
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