A Comprehensive Survey on Methods Implemented For Intruder Detection System

Authors

  • B Kiranmai Dept. of CSE, Nishitha College of Engg. & Tech., Greater Hyderabad, India
  • A Damodaram Dept. of CSE and Director AAC, JNTUH, Hyderabad, India

Keywords:

Intruder Detection System, Data Mining, Kddcup99

Abstract

Intrusion recognition is the act of discovering undesirable visitors on a system or a system. An IDS can be a piece of set up software or a physical equipment that watches system visitors in order to identify undesirable action and activities such as unlawful and harmful visitors, visitors that goes against security plan, and visitors that goes against appropriate use policies. Intruder detection system can be implemented using various data mining approaches. This paper summarizes intrusion motives and some of the methods used and implemented for intrusion detection system. This paper also reviewed about processing environment and type of data required for evaluation of Intruder detection system.

References

Asmaa shaker,Ashroor 2011 International conference on Future Information Technology IPCSIT vol.13 (2011) " (2011) IACSIT Press, Singapore.

Singh, S. and S. Kandula, "Argus - a distributed network-intrusion detection system," Undergraduate Thesis, Indian Institute of Technology, May 2001.

Jiawei Han and Micheline Kamber Data Mining Concepts and Techniques Second Edition Morgan Kauffman Publishers ,2006

Shaik Akbar, Dr.K. Nageswara Rao, Dr.J.A. Chandulal IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.8, August 2011pp 138-144

Mrutyunjaya Panda, Manas Ranjan Patra IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.12, December 2007 pp 258- 263

P.Jenson, "Bayesian networks and decision graphs", Springer, New-york, USA, 2001.

Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung 0-7803-7278-6/02 "2002 IEEE

Nani Yasmin1, Anto Satriyo Nugroho2, Harya Widiputra3," Optimized Sampling with Clustering Approach for Large Intrusion Detection Data", International Conference on Rural Information and Communication Technology 2009 Pp.56-60

Yu Guan and Ali A. Ghorbani, Nabil Belacel,"Y-Mean: A Clustering method For Intrusion Detection", 1CCECE 2003, pp.1-4

Fangfei Weng, Qingshan Jiang, Liang Shi, and Nannan Wu,"An Intrusion Detection System Based on the Clustering Ensemble", IEEE International workshop on 16-18 April 2007,pp.121 " 124

Kusum kumara Bharati,Sanyam Shukla, Swetha Jain Special Issue of IJCCT Vol.1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5 August 2010

Wenkee Lee, Salvatore J. Stolfo, Kui W. Mok c 2000 Kluwer Academic Publishers. Printed in Netherlands.

Rahimeh Rouhi , Farshid Keynia, Mehran Amiri Journal of Computer Sciences and Applications, 2013, Vol. 1, No. 3, 33-38

Shengi YiJiang,Xiaoyu Song, Hui Wang, Jian-Jun Han,Qing-Hua Li Science direct " 2005 Elsevier pp 802-810

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Published

2014-08-30

How to Cite

[1]
B. Kiranmai and A. Damodaram, “A Comprehensive Survey on Methods Implemented For Intruder Detection System”, Int. J. Comp. Sci. Eng., vol. 2, no. 8, pp. 70–73, Aug. 2014.

Issue

Section

Review Article