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Hybrid Intelligent Intrusion Detection System using Bayesian and Genetic Algorithm (BAGA): Comparitive Study
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 99 - Number 2 |
| Year of Publication: 2014 |
| Authors: Y V Srinivasa Murthy, Kalaga Harish, D K Vishal Varma, Karri Sriram, B V S S Revanth |
10.5120/17342-7808
|
Y V Srinivasa Murthy, Kalaga Harish, D K Vishal Varma, Karri Sriram, B V S S Revanth . Hybrid Intelligent Intrusion Detection System using Bayesian and Genetic Algorithm (BAGA): Comparitive Study. International Journal of Computer Applications. 99, 2 ( August 2014), 1-8. DOI=10.5120/17342-7808
Abstract
Intrusion detection system (IDS) is one of the emerging techniques for information security. Security mechanisms for an information system should be designed to prevent unauthorized access of system resources and data. Many intelligent learning techniques of machine learning are applied to the large volumes of data for the construction of an efficient intrusion detection system (IDS). This paper presents an overview of intrusion detection system and a hybrid technique for intrusion detection based on Bayesian algorithm and Genetic algorithm. Bayesian algorithm classifies the dataset into various categories to identify the normal/ attacked packets where as genetic algorithm is used to generate a new data by applying mutation operation on the existing dataset to produce a new dataset. Thus this algorithm classifies KDD99 benchmark intrusion detection dataset to identify different types of attacks with high detection accuracy. The experimental result also shows that the accuracy of detecting attacks is fairly good.
References
- KDD CUP - 99 task description. https://kdd. ics. uci. edu/databases/kddcup99/task. html.
- James Cannady. Artificial neural networks for misuse detection. In National information systems security conference, pages 368–81.
- Mark Crosbie and Gene Spafford. Applying genetic programming to intrusion detection. In Working Notes for the AAAI Symposium on Genetic Programming, pages 1–8. MIT, Cambridge, MA, USA: AAAI, 1995.
- Dewan Md Farid, Mohammad Zahidur Rahman, and Chowdhury Mofizur Rahman. Adaptive intrusion detection based on boosting and na¨?ve bayesian classifier. International Journal of Computer Applications, 24(3):12–19, 2011.
- Thorsten Joachims. Making large scale svm learning practical. 1999.
- John McHugh. Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM transactions on Information and system Security, 3(4):262–294, 2000.
- Srinivas Mukkamala and Andrew Sungand Ajith Abraham. Cyber security challenges: designing efficient intrusion detection systems and antivirus tools. Vemuri, V. Rao, Enhancing Computer Security with Smart Technology. (Auerbach, 2006), pages 125–163, 2005.
- Srinivas Mukkamala, Andrew H Sung, and Ajith Abraham. Intrusion detection using ensemble of soft computing paradigms. In Intelligent Systems Design and Applications, pages 239–248. Springer, 2003.
- Srinivas Mukkamala, Andrew H Sung, and Ajith Abraham. Intrusion detection using an ensemble of intelligent paradigms. Journal of network and computer applications, 28(2):167–182, 2005.
- Sandhya Peddabachigari, Ajith Abraham, Crina Grosan, and Johnson Thomas. Modeling intrusion detection system using hybrid intelligent systems. Journal of network and computer applications, 30.
- J Ross Quinlan. Decision trees and decision-making. Systems, Man and Cybernetics, IEEE Transactions on, 20(2):339–346, 1990.
- John Ross Quinlan. C4. 5: programs for machine learning, volume 1. Morgan kaufmann, 1993.
- Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali-A Ghorbani. A detailed analysis of the kdd cup 99 data set. In Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications 2009, 2009.
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