Intrusion Detection Based On Fuzzy Logic Approach Using Simplified Swarm Optimization
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2014 by IJCTT Journal | ||
| Volume-13 Number-1 |
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| Year of Publication : 2014 | ||
| Authors : S. Revathi , Dr. A. Malathi | ||
| DOI : 10.14445/22312803/IJCTT-V13P105 |
S. Revathi , Dr. A. Malathi."Intrusion Detection Based On Fuzzy Logic Approach Using Simplified Swarm Optimization". International Journal of Computer Trends and Technology (IJCTT) V13(1):19-22, July 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
The intrusion is becoming more essential for effective defense against attacks that are constantly changing in magnitude and complexity. Mainly intrusion detection relies on the extensive knowledge of security experts. The paper proposed a new detection mechanism as Fuzzy Intrusion Detection Engine (FIDE) that uses fuzzy logic to access network data. FIDE uses fuzzy analyzer engine to evaluate inputs and generate alerts for security administrators. The FIDE act as a fuzzy classifier, whose knowledge base is act as fuzzy “if-then” rule. This paper describes the components of FIDE architecture, and explains the benefit of fuzzy rule that improve fuzzy sets. Finally, in order to obtain the best result Simplified Swarm Optimization is used to optimize the structure of FIDE. The simulation of the proposed system is trained and tested with actual real time network data. The FIDE IDS can detect a wide range of common attack types. The proposed system shows high accuracy in identifying attacks.
References
1. H. Debar, M. Dacier, A. Wespi, Towards a taxonomy of intrusion detection systems, Computer Networks 31 (1999) 805–822.
2. G. Macia Fernandez and E. Vazquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges”, Computers & Security, Vol. 28, No. 1-2, pp. 18-28, February-March 2009.
3. J. Luo, “Integrating fuzzy logic with data mining methods for intrusion detection,” Master’s thesis, Dept. Comput. Sci., Mississippi State Univ., Starkville, MS, 1999.
4. S. Axelsson, 2000. Intrusion detection systems: a survey and taxonomy, Department of Computer Engineering, Chalmers University, Report No. 99-15.
5. S.-J. Han and S.-B. Cho, “Evolutionary neural networks for anomaly detection based on the behavior of a program,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 3, pp. 559–570, Jun. 2006.
6. Q. Tran, H. Duan, and X. Li, “One-class support vector machine for anomaly network traffic detection,” presented at the 2nd Netw. Res. Workshop 18th APAN, Cairns, Australia, Jul. 2004.
7. J. Gomez, D. Dasgupta, “Evolving fuzzy classifiers for intrusion detection”, in: Proceeding of 2002 IEEE Workshop on Information Assurance, United States Military Academy, West Point, NY, USA, 2001, pp. 68–75.
8. D. Barbara, J. Couto, S. Jajodia, L. Popyack, and N. Wu, “ADAM: Detecting intrusions by data mining,” in Proc. 2nd Annu. IEEE Workshop Inf. Assur. Secur., New York, Jun. 2001, pp. 11–16
9. D. E. Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning. Reading”, MA: Addison-Wesley, 1989.
10. A. A. Freitas, “Data Mining and Knowledge Discovery with Evolutionary Algorithms”. New York: Springer-Verlag, 2002.
11. W. Lee and S. Stolfo, “Data mining approaches for intrusion detection,” in Proc. 7th USENIX Secur. Symp., San Antonio, TX, Jan. 1998, pp. 79–83.
12. W. Lee, S. Stolfo, and K. Mok, “A Data Mining Framework for Building Intrusion Detection Model”, In Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, pp. 120-132, 1999.
13. Arif Jamal Malik, Waseem Shahzad, Farrukh Aslam Khan. “Binary PSO and Random Forests Algorithms for PROBE attacks Detection in a network”. In Proceedings of IEEE Congress on Evolutionary Computation, 662-668, (2011).
14. Yao Liu, Yuk Ying Chung, Wei-Chang Yeh: “Simplified Swarm Optimization with Sorted Local Search for golf data classification”. IEEE Congress on Evolutionary Computation (2012): 1-8.
15. S.Revathi, A.Malathi, “Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 75– No.6, August 2013.
16. M. Davarynejad, M.-R. Akbarzadeh-T, IEEE Senior Member, N. Pariz, “A Novel General Framework for Evolutionary Optimization: Adaptive Fuzzy Fitness Granulation”.
17. Safaa Zaman, Mohammed El-Abed, Fakhri Karray, “Features Selection Approaches for Intrusion Detection Systems based on Evolution Algorithms”, ICUIMC(IMCOM)’13, Kota Kinabalu, Malaysia, ACM 978-1-4503-1958-4…. January 17–19, 2013.
Keywords
Fuzzy Logic, intrusion Detection, Simplified Swarm Optimization, FIDE.