Detecting Attacks in Wireless Sensor Network Using Genetic Algorithms

Wireless Sensor Network (WSN) technology has attracted much attention in recent years and enables new applications but it requires nonconventional paradigms for the protocol design due to limited amount of energy source, memory, and computation constraints. This technology is used in military, medicine, environmental and industrial monitoring applications. Many attempts have been done, to promote these networks in order to meet objectives such as increasing lifetime, speed of transfer information, quality of service and security. One of the challenging subjects in these networks is surveying the security attacks to the network layer and finding a solution for them. Thus, we need to establish a method which can detect the attack and disable the attacker from the network access, by using the minimum battery consumption, and with a simple, effective and robust algorithm to perform our objectives. The purpose of this research is to identify the threats detected by clustering genetic algorithm in the clustered sensor networks, which will lead to prolong the network lifetime. In addition, the optimal routing is done by applying fuzzy function. The simulation results show that the simulated genetic algorithm has speededup the detection and improved the energy consumption cost.