Protect Ad-Hoc Network Through Artificial Neural Network
In present paper, we are providing enhanced security to Manet using the ACO through Artificial Neural Networks. Such techniques include flow and congestion control, where nodes send packet acknowledgements from destination nodes to either ramp-up or decrease packet transmission speed. The area of interest in this report concentrates on the idea of network routing and routing tables. These tables hold information used by a routing algorithm to make a local forwarding decision for the packet on the next node it will visit in order to reach its final destination.
One of the issues with network routing (especially in very large networks such as the internet) is adaptability. Not only can traffic be unpredictably high, but the structure of a network can change as old nodes are removed and new nodes added. This perhaps makes it almost impossible to find a combination of constant parameters to route a network optimally.
The neural network system is trained for some data and then file used for storing the data is eliminated, resulting confidentiality of connection. Thus the method makes the system more secure.
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