Detecting Malicious Behavior using GLM |
Author(s): |
P. Beula Arul Felcia , Bharathidasan University |
Keywords: |
Detecting the malicious data overhead security, Delay Mechanism, nearest neighboring algorithm, Generalized Linear Model algorithm, Secure data aggregations |
Abstract |
Wireless sensor networks often consist of a huge processing based on a security system. Collision nodes are appeared access the malicious data. Data aggregation with collusion aggregated in an energy efficient manner so that network malicious data injections are enhanced. Data aggregation process can enhance the robustness and accuracy of information which is obtained by sophisticated regression methods. Indeed, the development of effective and efficient defense mechanisms to those attacks must be speaking at every period of the system design. We utilize Markov tie procedure to break down the proposed two models and correlation demonstrates that the two level correspondence models devours less power and is more suitable than single level correspondence model on the force transmission line observing frameworks. It has been more security for wireless sensor network overcome between malicious data. This paper tends to outline the major aspects of wireless sensor networks security. Some related works and proposed schemes concerning security in these networks are also conversed. And to accomplish, conclude the paper delineating the research challenges and future trends in the research in wireless sensor network security. |
Other Details |
Paper ID: IJSRDV4I60416 Published in: Volume : 4, Issue : 6 Publication Date: 01/09/2016 Page(s): 798-802 |
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