Blackhole Attack Detection in Wireless Sensor Network Using Backpropagation Algorithm

The usage of Wireless Sensor Networks (WSN) in commercial and consumer applications is common. WSN places a high priority on privacy, making it crucial to protect any utilized, transferred, or stored data. Nevertheless, since wireless communications use a broadcasting mechanism. WSNs have no security safeguards, they can be easily manipulated by intruders. Hence, a hacker has exposure to all transactions and is able to listen in, transmit destructive signals, replay previous messages, or commandeer a sensor-node. In this research we are focusing on the blackhole attack, which is one of the most vulnerable attacks: With this kind of attack, the attacker discards data packet that passes through him. As a result, each packet that passes through this malicious intermediate node will experience partial or complete data loss. By employing the Back Propagation algorithm in Artificial Neural Network for WSN we are successfully identifying malicious nodes. Through this work, we are able to improve the accuracy rate for identifying malicious nodes by 77.78.