An effective DDoS attack mitigation strategy for IoT using an optimization-based adaptive security model

The Internet of Things enables the creation of transmitted use cases for interconnected devices and complementary channels. The varied structure of it creates additional security needs and problems. In particular, the safeguards used in the IoT should adjust to the changing environment. One of the major dangers to the World Wide Web (WWW) things is Distributed Denial of Service (DDoS). Therefore, in this work, an intelligent Game Theory-based Adaptive security (GT-AS) mathematical model was developed to maximize the effectiveness of DDoS attack mitigation. Moreover, this strategy can strongly derive the five parameters such as energy channel, memory, intruder, and hybrid. These all can achieve a stronger defense posture against DDoS attacks from the newly designed IoT. Consequently, the Recurrent Bat (RB) framework is developed to classify the nodes into two classes such as trusted node and malicious node. In addition, the proposed frameworks analyze how protection effectiveness and energy consumption interact when evaluating adaptive security techniques. To analyze the effectiveness of the suggested paradigm, researchers also give the outcomes of simulation experiments. Researchers demonstrate that, in comparison to existing models, the developed approach has increased the lifespan of the connected objects by 47 %. Also, the developed strategy has attained better accuracy and lower error rates when comparing traditional strategies. Moreover, the packet delivery ratio is 60 KB, energy consumption is 116 KJ, Mean Location Error is 0.078 and resource usage is 148.