Energy optimization using adaptive control algorithm to enhance the performance of SDN_IOT environment

Cybersecurity attacks are evolving against the normal flow oftraffic/data in the network. The information stolen by the intruders/attackers can interrupt network services and compromise the integrity of the IoT devices. The most prevalent extortions to SDN-IoT networks are Distributed Denial of Service (DDoS) attacks, which flood the attack request until the server/device crashes/shuts down. This paper presents an algorithm for detecting DDoS attacks earlier from the network using the entropy method and mitigating the attacks earlier using stochastic techniques. The detection and mitigation of DDoS attacks from the SDN_IoT environment calculates the energy utilized by the devices connected to the network using Adaptive optimization techniques. This technique concludes the optimum dissemination of network resources and the deployment of defense mechanisms to mitigate the control of attacks. The efficacy of the suggested approach is proven by comprehensive simulations that use realistic attack scenarios and representative SDN-IoT network topologies. The system reduces energy consumption by 18%, improves detection accuracy by 2% (from 97.5% to 99.4%), and lowers the false positive rate to 0.01% compared to existing algorithms. The suggested approach also improves throughput by 2.6%, decreases detection time by 70%, and reduces CPU use by 15%, all of which contribute to quicker mitigation of DDoS attacks. The method’s capacity to optimize network security and resource consumption is shown by these results, establishing it as a strong option for safeguarding SDN-IoT systems.