Enhanced Intrusion Detection System using Hybrid Machine Learning Approach

Pavan Singhal, Gajendra Singh

Abstract


Modern business has run on technology and it is based on communication and consequently the gigantic speed of the today’s internet or communication is the cause of the advancement in telecommunication and semiconductor technologies together. Billions of users are accessing the internet hundreds of time in a day. Due to flexibility and ease of networking services security is the chief concern. To get protected Intrusion Detection and Preventions System are the best option to assure security. In this article Anomaly based IDPS has been proposed and evaluated using hybrid machine learning approach. Machine learning sub branch of the soft computing had evolved since last decade has present more promising solution in the field of the security (host and network). Various methods of machine learning have been tested to produces better results in detection of intrusive activities. Classification (KNN) and evidence theory (DS) is types of machine learning approach and support to provide better solution in this direction. Proposed method has adopted the idea of KNN and DS Theory to fasten the detection speed, achieving better efficiency and accuracy with low false positive and negative ratio. Obtained results have achieved the accuracy about 97.47% and false ratio has minimized and limited it to 1.2 and 1.3.

Keywords


DS, DST, IDS, IDPS, KDD, KNN, Machine Learning.

References


José Camacho, Pablo Padilla, Pedro García-Teodoro and Jesús Díaz-Verdejo “A generalizable dynamic flow pairing method for traffic classification”, Elsevier science direct, Computer Networks 57 (2013) 2718–2732, 2013.

Lunt, T. 1993. Detecting intruders in computer systems. In Proceedings of 1993 conference on auditing and computer technology. (Downloaded from http://www2.csl.sri.com/nides/index5.html on 3 February 1999.)

Mukherjee, B., L. Heberlein, and K. Levitt. 1994. Network intrusion detection. IEEE Network, May/June, 26-41.

Mansour Sheikhan and Zahra Jadidi, “ Misuse Detection Using Hybrid of Association Rule Mining and Connectionist Modeling”, World Applied Sciences Journal 7 (Special Issue of Computer & IT): 31-37, 2009.

R. Shanmugavadivu Dr. N. Nagarajan “NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC”, Indian Journal of Computer Science and Engineering (IJCSE).

Nannan Lu; Mabu, S.; Wenjing Li; Hirasawa, K.; Grad. Sch. of Inf., Waseda Univ., Fukuoka, Japan “Hybrid rule mining based on fuzzy GNP and probabilistic classification for intrusion detection”, SICE Annual Conference 2010.

Shangping Dai; Li Gao; Qiang Zhu; Changwu Zhu; Hua Zhong Normal Univ., Wuhan, “A Novel Genetic Algorithm Based on Image Databases for Mining Association Rules”, Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS.


Full Text: PDF

Refbacks

  • There are currently no refbacks.




 


All Rights Reserved © 2012 IJARCSEE


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.