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Abstract
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Article Information:
Detection of Attacks on MAODV Association Rule Mining Optimization
A. Fidalcastro and E. Baburaj
Corresponding Author: A. Fidalcastro
Submitted: October 10, 2014
Accepted: November 26, 2014
Published: February 25, 2015 |
Abstract:
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Current mining algorithms can generate large number of rules and very slow to generate rules or generate few results, omitting interesting and valuable information. To address this problem, we propose an algorithm Optimized Featured Top Association Rules (OFTAR) algorithm, where every attack have many features and some of the features are more important. The Features are selected by genetic algorithm and processed by the OFTAR algorithm to find the optimized rules. The algorithm utilizes Genetic Algorithm feature selection approach to find optimized features. OFTAR incorporate association rules with several rule optimization techniques and expansion techniques to improve efficiency. Increasing popularity of Mobile ad hoc network users of wireless networks lead to threats and attacks on MANET, due to its features. The main challenge in designing a MANET is protecting from various attacks in the network. Intrusion Detection System is required to monitor the network and to detect the malicious node in the network in multi casting mobility environment. The node features are processed in Association Analysis to generate rules, the generated rules are applied to nodes to detect the attacks. Experimental results show that the algorithm has higher scalability and good performance that is an advantageous to several association rule mining algorithms when the rule generation is controlled and optimized to detect the attacks.
Key words: Association analysis, black hole attack, data mining, genetic algorithm, intrusion detection system, MANET, spoofing attack, top rules
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Cite this Reference:
A. Fidalcastro and E. Baburaj, . Detection of Attacks on MAODV Association Rule Mining Optimization. Research Journal of Applied Sciences, Engineering and Technology, (6): 454-459.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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