Applied Mathematics & Information Sciences

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The tremendous growth of the pervasive network and its utilization, in the Internet of Things (IoT), is purposefully explored around the world. The Internet of things has an enormous attraction for contender, and is effortlessly attacked due to destitute resource and imperfect distribution of things. The distributed denial of service attacks are fetching an increasingly, continual hassle into the network. Security possess significant insistence in the Internet of Things (IoT). In this paper, an algorithm for malicious user identification named as Flooding Distributed Denial of Service Attack Detection and Prevention Mechanism (FADM) is coined to protect the network, from ruining. The entropy-based approach for detection and bloom filter for prevention is used. A user is classified as malicious when its entropy value is low compared to threshold. The simulation outcome makes evident that the algorithm identifies the malicious user accompanied by elevated detection ratio, reduced false alarm ratio, and exceptional scalability.

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