Pattern Based Network Security Using Semi-Supervised Learning

Abstract

Network security is becoming increasingly important in today’s internetworked systems. With the development of internet, its use on public networks, the number and the severity of security threats has increased significantly. Intrusion Detection System can provide a layer of security to these systems. The goal of intrusion detection system is to identify entities who attempt to subvert in-place security controls. The field of machine learning is gaining increasing attention in the development of intrusion detection systems. The machine learning techniques used for solving intrusion detection problem can be broadly classified into three broad categories: Unsupervised, supervised and semi-supervised. The supervised learning method exhibits good classification accuracy for known attacks. But it requires large amount of training data. In real world the availability of labeled data is time consuming and costly. An emerging field of semisupervised learning offers a promising direction for further research. So in this work we propose a semi-supervised approach for pattern based IDS to improve performance of supervised approach. The experimentation is performed on KDD CUP99 dataset.