Abstract
: Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we use the probabilistic neural network approach that gives more efficient and accurate results as comparison to the clustering algorithm in the previous systems was used. The performance of PNN (probabilistic neural network) approach is improved for identifying the particular attack like as wormholes, black holes and selfish
Highlights
Network security is attracting more and more attention
Based on the purpose and actuality of simulation of network security, this paper puts forward a simulation method of network security using system dynamics
After giving the steps of system dynamics simulation of network security, this paper has simulated the attack of worm using system dynamics[1]
Summary
Network security is attracting more and more attention. Simulation is a better choice to research the problems of network security because of their high complexity. A node maintains its own routing table, storing all nodes in the network, the distance and the hop to them. Every node sends its neighbors‟ periodically its whole routing table They can check if there is a useful route to another node using this neighbor as hop. One router in each of them is responsible to operate the AODV for the whole subnet and serves as a default gateway It has to maintain a sequence number for the whole subnet and to forward every package. In AODV the routing table is expanded by a sequence number to every destination and by time to live for every entry.
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