A Convolutional Neural Network for Network Intrusion Detection System
Leila; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia Mohammadpour, Chun Yong; School of Information Technology, Monash University Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Selangor, Malaysia Chong, Teck Chaw; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia Ling, Chee Sun; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia Liew
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Abstract
System administrators can benefit from deploying Network Intrusion Detection Systems (NIDS) to find potential security breaches. However, security attacks tend to be unpredictable. There are many challenges to develop a flexible and effective NIDS in order to prevent high false alarm rates and low detection accuracy against unknown attacks. In this paper, we propose a deep learning method to implement an effective and flexible NIDS. We used a convolutional neural network (CNN), an advanced deep learning technique, on NSL-KDD, a benchmark dataset for network intrusion. Our experimental results of a 99.79% detection rate when compared against the NSL-KDD test dataset show that CNNs can be applied as a learning method for Intrusion Detection Systems (IDSs)