A NOVEL EVALUATION APPROACH TO FINDING LIGHTWEIGHT MACHINE LEARNING ALGORITHMS FOR INTRUSION DETECTION IN COMPUTER NETWORK

International Journal of Network Security & Its Applications (IJNSA)

ISSN 0974 - 9330 (Online); 0975 - 2307 (Print)

http://airccse.org/journal/ijnsa.html

Volume 07, Number 06 - November 2015

Article -    A Novel Evaluation Approach to Finding Lightweight Machine Learning Algorithms for                      Intrusion Detection in Computer Network.

Authors -  Yuchen Wang and Qiongfang Huang, Zhejiang University of Technology, China Shuxiang                    Xu, University of Tasmania, Australia

Abstract :

               Building practical and efficient intrusion detection systems in computer network is important in industrial areas today and machine learning technique provides a set of effective algorithms to detect network intrusion. To find out appropriate algorithms for building such kinds of systems, it is necessary to evaluate various types of machine learning algorithms based on specific criteria. In this paper, we propose a novel evaluation formula which incorporates 6 indexes into our comprehensive measurement, including precision, recall, root mean square error, training time, sample complexity and practicability, in order to find algorithms which have high detection rate, low training time, need less training samples and are easy to use like constructing, understanding and analyzing models. Detailed evaluation process is designed to get all necessary assessment indicators and 6 kinds of machine learning algorithms are evaluated.Experimental results illustrate that Logistic Regression shows the best overall performance. 

for more please visit : http://aircconline.com/ijnsa/V7N6/7615ijnsa01.pdf