RLMIDS: RULE LEARNER AND MULTITHREADING TECHNIQUE FOR INLINE INTRUSION DETECTION SYSTEM WITH GENETIC ALGORITHM FOR HIGH SPEED NETWORK

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Published: 2015-09-11

Page: 148-161


D. P. GAIKWAD *

Department of Computer Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India

RAVINDRA C. THOOL

Department of Computer Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India

*Author to whom correspondence should be addressed.


Abstract

Intrusion detection system plays the vital role in identifying unauthorized user, abnormal packets and malicious code in network. The researchers have been proposed many techniques and methods of intrusion detection system. The challenging task in intrusion detection system is to find out appropriate method which produces low false positive rate and high classification accuracy. The rule based method is advanced and simple. The performance of rule based intrusion detection system is depending on the rules generated by rule learner. Rule formation process is slow and time consuming task due to huge amount of packets in networks. In this paper, a novel architecture of intrusion detection system has been presented. The system is implemented by using rule learner with multi-threading technique, which we call RLMIDS. In this implementation, the Ripple Down Rule learner is used as a classifier and Genetic Algorithm as a feature selection method with Multithreading technique. The advantages of parallel processing feature of multi-threading help to handle the heavy traffic in high speed network. The cache management module of the system is used to reduce the memory access rate. The proposed RLMIDS is evaluated in terms of classification accuracy and false positive rate. The performance evaluation results show that the proposed RLMIDS outperforms existing standard classifier. The logging mechanism of proposed system can be used to reprocess and analyses logged packets in future for investigation and forensic purpose. It is also found that the time required to generate rules from the data set is lower as compared to the model building time of existing classifier in intrusion detection system.

Keywords: Multi-threading, rule learner, cache updating, false positive, classification Accuracy


How to Cite

GAIKWAD, D. P., & THOOL, R. C. (2015). RLMIDS: RULE LEARNER AND MULTITHREADING TECHNIQUE FOR INLINE INTRUSION DETECTION SYSTEM WITH GENETIC ALGORITHM FOR HIGH SPEED NETWORK. Asian Journal of Mathematics and Computer Research, 7(2), 148–161. Retrieved from https://ikprress.org/index.php/AJOMCOR/article/view/391

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