Research Article
Pattern Based IDS Using Supervised, Semi-supervised and Unsupervised Approaches
@INPROCEEDINGS{10.1007/978-3-642-27299-8_57, author={Vinod Pachghare and Vaibhav Khatavkar and Parag Kulkarni}, title={Pattern Based IDS Using Supervised, Semi-supervised and Unsupervised Approaches}, proceedings={Advances in Computer Science and Information Technology. Networks and Communications. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part I}, proceedings_a={CCSIT PART I}, year={2012}, month={11}, keywords={Intrusion Detection System Pattern Based Algorithm Security supervised learning semi-supervised learning Machine Learning Neural Networks}, doi={10.1007/978-3-642-27299-8_57} }- Vinod Pachghare
Vaibhav Khatavkar
Parag Kulkarni
Year: 2012
Pattern Based IDS Using Supervised, Semi-supervised and Unsupervised Approaches
CCSIT PART I
Springer
DOI: 10.1007/978-3-642-27299-8_57
Abstract
Intrusion detection aims at distinguishing the behavior of the network. Due to rapid development of attack pattern, it is necessary to develop a system which can upgrade itself according to new attacks. Also detection rate should be high since attack rate on the network is very high. In response to this problem, Pattern Based Algorithm is proposed which has high detection rate and low false alarm rate. The work is divided into three parts: supervised approach, semi-supervised and unsupervised approach. Besides supervised learning approach, semi-supervised learning has attracted much attention in pattern recognition and machine learning for intrusion detection. Most of the semi supervised algorithms used for intrusion detection are binary classifiers, but our approach is to classify the data into multiclass. Our experimental results on KDD cup data set shows that the performance of the proposed method is more effective.