Reinforcement based Cognitive Algorithms to Detect Malicious Node in Wireless Networks
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10.5120/19273-0990 |
G Sunilkumar, Thriveni J, K R Venugopal, Manjunatha C and L M Patnaik. Article: Reinforcement based Cognitive Algorithms to Detect Malicious Node in Wireless Networks. International Journal of Computer Applications 109(16):29-34, January 2015. Full text available. BibTeX
@article{key:article,
author = {G Sunilkumar and Thriveni J and K R Venugopal and Manjunatha C and L M Patnaik},
title = {Article: Reinforcement based Cognitive Algorithms to Detect Malicious Node in Wireless Networks},
journal = {International Journal of Computer Applications},
year = {2015},
volume = {109},
number = {16},
pages = {29-34},
month = {January},
note = {Full text available}
}
Abstract
The growth of wireless communication technologies and its applications leads to many security issues. Malicious node detection is one among the major security issues. Adoption of cognition can detect and Prevent malicious activities in the wireless networks. To achieve cognition into wireless networks, we are using reinforcement learning techniques. By using the existing reinforcement techniques, we have proposed GreedyQ cognitive (GQC) and SoftSARSA cognitive (SSC) algorithms for malicious node detection and the performances among these algorithms are evaluated and the result shows SSC algorithm is best algorithm. The proposed algorithms perform better in malicious node detection as compared to the existing algorithms.
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