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Wrapper based Intrusion Detection System with Duration and Local Area Network Denial Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 123 - Number 15 |
| Year of Publication: 2015 |
| Authors: Amol A. Dhiwar, Girish K. Patnaik |
10.5120/ijca2015905746
|
Amol A. Dhiwar, Girish K. Patnaik . Wrapper based Intrusion Detection System with Duration and Local Area Network Denial Features. International Journal of Computer Applications. 123, 15 ( August 2015), 23-28. DOI=10.5120/ijca2015905746
Abstract
The use of internet has become a popular way of getting connected with each other, as a result networking attacks get increased. The networking attacks are termed as intrusions that are based on the values of features. Features based Intrusion Detection Systems (IDS), mostly used for Denial of Service (DoS) attacks, have low response in terms of intrusion detection because of missing Local Area Network Denial (LAND) and duration features. Hence, precise security of a system is not assured without considering LAND and duration features. In order to minimize DoS attacks and to make the system more secured, it warrants additional features. All the features are having their certain values that indicate the presence or absence of an intrusion. An existing genetic algorithm has considered 16 features for intrusion detection but, still some DoS and Remote to Local (R2L) attacks are not covered in it. These attacks depend on duration and LAND features of dataset. In the proposed work these two features are focused and extracted using genetic algorithm so that detection response of IDS is improved.
References
- S.S.Sindhu, S. Geetha and A. Kannan, “Decision Tree based light weight intrusion detection using a wrapper approach”, Expert System with Applications, 2012, vol.39, pp.129-141.
- K.K.Gupta, B.Nath and R. Kotagiri, “Layered approach using conditional random field for intrusion detection”, IEEE Transaction on Dependable and Secured Computing, 2010, vol.7, pp.35-47.
- D.S.Mukharjee and N. Sharma, “Intrusion detection using naïve bayes classifier with feature reduction”, Elsevier Publications, 2012, vol.4, pp.119-128.
- M.S.Hoque, M.A.Mukit and M.A.N.Bikas, “An implementation of intrusion detection system using genetic algorithm” International Journal of Network Security and It’s Applications, 2012, vol.4, pp.109-120.
- M.M.M.Hassan, “Network intrusion detection system using genetic algorithm and fuzzy logic”, International Journal of Innovative Research in Computer and Communication Engineering, 2013, vol.1, pp.1435-1445.
- A.A.Olusola, A.S.Oladele, D.O.Abosede, “Analysis of kdd 99 intrusion detection dataset for selection of relevance features”, Proceeding of World Congress on Engineering and Computer Science, 2011, vol.1, pp. 978-988.
- D.Jayalutchumy and D.P.Thambiduri, “Web mining research issues and feature directions a survey”, International Organization of Scientific Research Journal of Computer Engineering, 2013, vol.4, pp.20-27.
- P.Mehataa, B. Parekh, K.Modi and P. Solanki, “Web personalization using web mining: concept and research issue”, International Journal of Information and Education Technology, 2012, vol.2, pp.510-512
- M. Yadav and P.Mittal. “Web mining: an introduction”, Intrnational Journal of Advanced Research in Computer Science and Software Engineering, 2013, vol.3 pp. 683-687.
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