An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier

Knowledge-Based Systems

Volumn 28, Issue , 2012, Pages 88-96

  Kavitha, B ^a   Karthikeyan, Dr S ^b   Sheeba Maybell, P ^c  

^a BHARATHIAR UNIVERSITY   (India)

^b College of Applied Sciences ^*  (Oman)

^c KARPAGAM UNIVERSITY   (India)

Author keywords

Fuzzy; Improvised genetic algorithm; Intrusion; KDD cup; Membership; Neutrosophic; Uncertainty

Indexed keywords

FUZZY; INTRUSION; KDD CUP; MEMBERSHIP; NEUTROSOPHIC; UNCERTAINTY;

COMPUTER CRIME; FUZZY LOGIC; GENETIC ALGORITHMS;

INTRUSION DETECTION;

EID: 84856226719     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2011.12.004     Document Type: Article

References (24)

1. Wei Liu, Research of Data Mining in intrusion detection system and the Uncertainty of the Attack, in: International Symposium on Computer Network and Multimedia Technology, 2009, CNMT 2009, 18-20 January 2009, pp. 1-4, ISBN:978-1-4244-5272-9, INSPEC Accession Number: 11069703.
2. D. Barbara, and S. Jajodia Applications of Data Mining in Computer Security 2002 Klnwer Academic Publishers
3. Oded Z. Maimon Lior Rokach Data Mining and Knowledge Discovery Handbook 2005 Springer
4. B. Kavitha, S. Karthikeyan, B. Chitra, Efficient intrusion detection with reduced dimension using data mining classification methods and their performance comparison, in: V.V. Das et al. (Eds.): BAIP 2010, CCIS 70, 2010, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 96-101.
5. Xinming Ou, S.R. Rajagopalan, S. Sakthivelmurugan, An empirical approach to modeling uncertainty in intrusion analysis, in: Computer Security Applications Conference, 2009, ACSAC '09, Dec. 2009.
6. Xinming Ou, S.R. Rajagopalan, S. Sakthivelmurugan, Kansas State Univ., Manhattan, KS, USA, An Empirical Approach to Modeling Uncertainty in Intrusion Analysis, in: Computer Security Applications Conference, 2009, ACSAC '09, pp. 7-11, December 2009.
7. Kok-Chin Khor, Choo-Yee Ting, S.-P. Amnuaisuk, Fac. of Inf. Technol., Multimedia Univ., Cyberjaya, in: Second Asia International Conference on A Probabilistic Approach for Network Intrusion Detection Modeling & Simulation, AICMS 08, 13-15 May 2008.
8. Ciza Thomas, N. Balakrishnan, Modified Evidence Theory for Performance Enhancement of Intrusion Detection Systems, SERC, Indian Institute of Science, India.
9. Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung, Intrusion detection using neural networks and support vector machines, in: Proceedings of IEEE Intrnational Joint Conference on Neural Networks, 2002, pp. 1702-1701.
10. Yingjiu Li et al., Enhancing profiles for anomaly detection using time granularities, Center for secure information systems, Journal of Computer Security, in press.
11. Susan Bridges, Rayford Vaughn, Fuzzy data mining and genetic algorithms applied to intrusion detection, in: Proceedings twenty third National Information Security Conference, October 1-19, 2000.
12. Stefan Axelsson, Intrusion detection systems: a survey and taxonomy, Technical Report No 99-15, Dept. of Computer Engineering, Chalmers University of Technology, Sweden, March 2000.
13. KDDCup99datasets,The UCI KDD Archive: http:/ kdd.ics.ucs.edu / databases /kddcup99 /kddcup99.html.
14. F. Smarandache, A unifying field in logics: neutrosophic logic, Multiple-Valued Logic/An International Journal 8 (3) (2002) 385-438, http://www.gallup.unm.edu/∼smarandache/eBook-neutrosophics2.pdf. (Pubitemid 36047938)
15. F. Smarandache, Neutrosophy, A new branch of philosophy, in multiple-valued logic, An International Journal 8(3) (2002) 297-384. (Pubitemid 36047937)
16. F. Smarandache (Eds.), Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic Set, Neutrosophic Probability and Statistics, University of New Mexico, Gallup Campus, Xiquan, Phoenix, 2002, p. 147, www.gallup.unm.edu/∼smarandache/NeutrosophicProceedings.pdf.
17. Grigorios N. Beligiannis, Georgios A. Tsirogiannis, Panayotis E. Pintelas, Restartings: a technique to improve classic genetic algorithms' performance, World Academy of Science, Engineering and Technology, 2005.
18. Jonatan Gomez, Dipankar Dasgupta, Olfa Nasraoui, Fabio Gonzalez, Complete Expression Trees for Evolving Fuzzy Classifier Systems with Gentic Algorithms and Application to Network Intrusion Detection, NAFIPS, 2002, 2002 - ieeexplore.ieee.org.
19. K. Atanassov Intuitionistic fuzzy sets Fuzzy Sets and Systems 20 1986 87 96
20. Charles Ashbacher, Introduction To Neutrosophic Logic, American Research Press, Rehoboth 2002.
21. Azzedine Boukerche, Renato B. Machado, Kathia R.L. Jucá, João Bosco M. Sobral, and Mirela S.M.A. Notare An agent based and biological inspired real-time intrusion detection and security model for computer network operations Computer Communications 30 13 2007 2649 2660 (Pubitemid 47368641)
22. Jin Yang, XiaoJie Liu, Tao Li, Gang Liang, and SunJun Liu Distributed agents model for intrusion detection based on AIS Knowledge-Based Systems 22 2 2009 115 119
23. Peng Yang, and Qingsheng Zhu Finding key attribute subset in dataset for outlier detection Knowledge-Based Systems 24 2 2011 269 274
24. B. Kavitha, S. Karthikeyan, and P. Sheeba Maybell Emerging intuitionistic fuzzy classifiers for intrusion detection system Journal of Advances in Information Technology 2 2 2011