Research Article

A Heuristic Approach for Efficient Detection of Intrusion

by  Naveen Mohan Prajapati, Atish Mishra, Praveen Bhanodia
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Issue 3
Published: May 2014
Authors: Naveen Mohan Prajapati, Atish Mishra, Praveen Bhanodia
10.5120/16321-5569
PDF

Naveen Mohan Prajapati, Atish Mishra, Praveen Bhanodia . A Heuristic Approach for Efficient Detection of Intrusion. International Journal of Computer Applications. 94, 3 (May 2014), 6-10. DOI=10.5120/16321-5569

                        @article{ 10.5120/16321-5569,
                        author  = { Naveen Mohan Prajapati,Atish Mishra,Praveen Bhanodia },
                        title   = { A Heuristic Approach for Efficient Detection of Intrusion },
                        journal = { International Journal of Computer Applications },
                        year    = { 2014 },
                        volume  = { 94 },
                        number  = { 3 },
                        pages   = { 6-10 },
                        doi     = { 10.5120/16321-5569 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2014
                        %A Naveen Mohan Prajapati
                        %A Atish Mishra
                        %A Praveen Bhanodia
                        %T A Heuristic Approach for Efficient Detection of Intrusion%T 
                        %J International Journal of Computer Applications
                        %V 94
                        %N 3
                        %P 6-10
                        %R 10.5120/16321-5569
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

The heuristic approach for efficient detection of intrusion is been proposed on this paper. The proposed framework uses new data preprocessing and filtration criteria which is data discretization to improve results of intrusion detection. It is more Accurate in comparison the existing methods. An overview of intrusion detection system is been presented. Also the present approaches for intrusion detection system are been described.

References
  • Litty Lionel, "Hypervisor-based Intrusion Detectio", Master of Science Graduate department of computer Science University of Torronto, 2005.
  • http://www. mendeley. com/research/naive-bayes-vs-decisiontrees-in-intrusion-detection-systems/#page-1
  • SrinivasMukkamalaa, Andrew H. Sunga and AjithAbrahamb Intrusion detection using an ensemble of intelligent paradigms?; www. elsevier. com/locate/jnca, January 2004.
  • "Data Mining Concepts and Techniques" byJiawei Han and MichelineKamber from Morgan Kaufman Publications.
  • Adriaan; "Introduction to Data Mining",Addison Wesley Publication
  • A. K. Pujari; "Data Mining Techniques"; University Press
  • Salem,karim "Revising the outputs of a decision tree with expert knowledge: Application to intrusion detection and alert correlation", 2012, ieee, p 452 -459.
  • Anupama Mishra, B. B. Gupta, R. C. Joshi," A Comparative study of Distributed Denial of Service Attacks, Intrusion Tolerance and mitigation Techniques" European Intelligence and Security Informatics Conference-2011.
  • Saketh Kumar shakkariG. Varalakshmi, "Detection of application layer DDOS attack for a popular website using delay of transmission", INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 10, Issue No. 2, 181 – 184-2011.
  • GU Jun. "Research on intrusion detection system based on KPCA and SVM". Journal of Computer Simulation, 2010, 27(7): 105-107.
  • Xiong Wen, Wang Cong. "Hybrid feature transformation based on modified particle swarm optimization and support vector machine". Journal of Beijing University of Posts and Telecommunications, 2009, 32(6): 24-28.
  • LI Zhong-long, SI Jin. "Distributed Denial of Service Analysis". Computer Knowledge and Technology, 2010, 6 (6): 2373-2374.
  • Xiang Xu ,Ding Wei , Yuelei Zhang. "Improved detection approach for DDOS attack based on SVM", 2011, IEEE
  • P. K. Agrawal , B. B. Gupta , Satbir Jain . "SVM Based scheme for Predicting Number of Zombies in a DDoS Attack" 978-0-7695-4406-9/11 $26. 00 © 2011 IEEE
  • KashifSaghar , William Henderson ,David Kendall , Ahmed Bouridane , "Applying formal modeling to detect Dos attack in wireless medium"
  • Xinfeng Ye , Santosh Singh " A soa approach to counter ddos attack" 2007 IEEE
  • http://en. wikipedia. org/wiki/ID3_algorithm
  • U. M. Fayyad and K. B. Irani, "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning," Proc. 13th Int. Joint Conf. Artificial Intelligence, pp. 1022-1027, 1993.
  • B. Hemada, K. S. Vijaya Lakshmi "Discretization Technique Using Maximum Frequent Values and Entropy Criterion" International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 11, November 2013.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

ID MDLP ID3 KDD CUP 99.

Powered by PhDFocusTM