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Wireless Sensor Networks are playing essential role across the world with its features and different kinds of development stages. Many researchers are contributing to build the WSN and extract its features. Intruders are involving in between the WSN signals and modify are reconstruct the entire network or framework. In past decades, Viruses and Trojans are the threats for the End user and anti-virus software are sufficient to face these threats. But now a days, passive and active attacks are occurring while transmission of network packages. Even though different kinds of threats in a WSN framework, we are contributing blackhole attack prevention mechanism using Deep Belief Network (DBN). DBN is the deep learning module and we are able to construct more number of hidden layers. We extracted the probability of black hole parameters and trained with original values for getting optimum results.
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