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A Novel Immunity inspired approach for Anomaly Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 15 |
| Year of Publication: 2014 |
| Authors: Praneet Saurabh, Bhupendra Verma |
10.5120/16418-6034
|
Praneet Saurabh, Bhupendra Verma . A Novel Immunity inspired approach for Anomaly Detection. International Journal of Computer Applications. 94, 15 ( May 2014), 14-19. DOI=10.5120/16418-6034
Abstract
Artificial Immune System (AIS) over the years has caught attention of researchers of various domains for complex problem solving. AIS model the procedure and methodologies of Biological Immune System (BIS) which protects the body from diverse attacks and different challenges. Scientists over the years are amazed with the appealing features of BIS that can be exploited. The most significant of them is its ability to distinguish self and non-self. This theory forms the basis of Negative Selection Algorithm (NSA) in AIS. NSA is competent for anomaly detection problems. From this perspective this research paper presents a Novel Immunity inspired approach for Anomaly Detection (NIIAD) with the feature of fine tuning. The main intention of adopting finetuning is to covering more self region and identifying non self region proficiently. Experimental results reflects high detection ratio with less false alarm and low overhead.
References
- B. Mukerjee, L. T. Heberlein and K. N. Levitt, 1994,"Network Intrusion Detection", IEEE Network, Vol. 8, No. 3, pp. 26-41.
- D. E. Denning, 1987, "An Intrusion-Detection Model", IEEE Transactions on Software Engineering, Vol. 13, No. 2, pp. 222-232.
- D. Dasgupta, 1999, "Immunity-based Intrusion Detection System: A General Framework", in Proceedings of 22nd National Information Systems Security Conference, Arlington, Virginia, USA, pp. 147- 160.
- D. Dasgupta and S. Forrest 1999, "An Anomaly Detection Algorithm Inspired by the Immune System", Chapter 14 in the book entitled Artificial Immune Systems and their Applications, Publisher: Springer-Verlag, pp. 262–277.
- F. Esponda, S. Forrest and P. Helman, 2003 "A Formal Framework for Positive and Negative Detection Schemes",IEEE Transactions on System, Man and Cybernetics, Vol. 34, No. 1, pp 357-373.
- F. Gonzalez, D. Dasgupta and J. Gomez, 2003 "The Effect of Binary Matching Rules in Negative Selection",Genetic and Evolutionary Computation Conference (GECCO), Chicago, pp. 383-403
- F. A. Gonzalez and D. Dasgupta "Anomaly Detection Using Real-Valued Negative Selection", Genetic Programming and Evolvable Machine,Vol. 4. No. 4, December 2003, pp. 383-403.
- KDD Cup DataSet, http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html
- K. Jungwon and P. Bentley, 1999, "The Human Immune System and Network Intrusion Detection", EUFIT 99, pp. 1244-1252.
- M. Ayara, J. Timmis, R. Lemos, L. N. de Castro and R. Duncan, 2002, "Negative Selection: How to Generate Detectors", 1st International Conference on Artificial Immune System (ICARIS), UK, pp
- M. F. Zafar, F. Naheed, Z. Ahmad and M. M. Anwar, "Network Security: A Survey of Modern Approaches", The Nucleus, 45 (1-2), 27 May 2008, pp 11-31.
- P. K. Harmer, P. D. Williams, G. H. Gunsch and G. B. Lamont, June 2002, "An arti?cial immune system architecture for computer security applications", IEEE transactions on evolutionary computation, Vol. 6, No. 3, pp. 252–280.
- P. Saurabh, B. Verma and S. Sharma, 2012, "Biologically Inspired Computer Security System: The Way Ahead", in Recent Trends in Computer Networks and Distributed Systems Security Communications in Computer and Information Science, Vol, 335, Springer, pp. 474-484.
- R. E. Overil, 2007, "Computational immunology and anomaly detection", Information Security Technical Report, Science Direct, Vol. 12, pp. 188-191.
- S. Forrest , A. S. Perelson, L. Allen and R. Cherukuri, 1994, "Self-nonself discrimination in a computer", IEEE Symposium on Research in Security and Privacy, IEEE Computer Society Press, Los Alamitos, CA, pp. 202–212.
- S. Ramakrishnan and S. Srinivasan, 2009, "Intelligent agent based artificial immune system for computer security—a review", Artificial Intelligence Review, Vol. 32, No. 1-4, pp 13–43.
- S. Forrest, S. A. Hofmeyr and A. Somayaji, 1997, "Computer Immunology," in Communications of the ACM, Vol. 40, No. 10, pp. 88–96.
- S. T. Powers, J. He ," A hybrid artificial immune system and Self Organising Map for network intrusion detection", in Information Sciences, Vol. 178, No. 15, August 2008, pp. 3024-3042.
- U. Aickelin, J. Greensmith and J. Twycross, 2004, "Immune system approaches to Intrusion Detection- A Review". ICARIS, Springer, pp. 316–329.
- W. Wang , X. Guan and X. L. Zhang, 2008, "Processing of massive audit data streams for real-time anomaly intrusion detection", Computer Communications, Vol. 31, No. 1, 15, pp. 58–72.
- Lincoln Laboratory, Massachusetts Institute of Technology, 1999 DARPA, Intrusion detection evaluation data set, Available at: http://www. ll. mit. edu/mission/communications/ist/corpora/ideval/data/1999data. html
- R. Lippmann, J. W. Haines, D. J. Fried , J. Korba, "The 1999 DARPA off-line intrusion detection evaluation", in Computer Networks, Vol. 34, No. 4, October 2000, pp. 579-595.
- S. W. Lin, K. C. Ying, C. Y. Lee and Z. J. Lee, 2012, "An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection",in Applied Soft Computing, Vol. 12, No. 10, pp. 3285-3290.
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