DoS attack detection and hill climbing based optimal forwarder selection

Wireless networks are becoming a more and more common form of networking and communication, with several uses in many industries. However, the rising popularity has also increased security risks, such as Denial of Service (DoS) attacks. To solve these issues, Denial of Service Attack Detection and Hill Climbing (DDHC) based optimal forwarder selection in Wireless Network. The suggested method seeks to efficiently identify DoS attacks and enhance network performance by preventing the communication hiccups brought on by such attacks. Fuzzy learning method is suggested to analyze trends and find DoS threats. The node bandwidth, connectivity, packet received rate, utilized energy and response time parameters to detect the node abnormality. This abnormality decides the node's future state and detects the DoS attacker. A fuzzy learning algorithm is proposed to detect DoS attacks, which increases attack detection accuracy and lowers false alarm rates. Using the Hill Climbing (HC) procedure, the proposed system transmits data from sender to receiver. Simulation results illustrate the DDHC mechanism increases the DoS attacker detection ratio and minimizes the false positive ratio. Furthermore, it raises the network throughput and reduces the Delay in the network