Enhanced method of ANN based model for detection of DDoS attacks on multimedia internet of things

Due to the huge flow of data and complications in mutable characteristics of the data, Distributed Denial of Services attacks existed in the Multimedia Internet of Things. Attacks over the IoT have become an increasing menace in recent time, which tries to hack or illegally tamper the streaming data available over the networks. On the other hand, there has been an increase in volume in research contributions to effectively counter these attacks and implement a strong defense mechanism. There have been numerous algorithms and frameworks implemented in recent times that are intelligent and soft computing-based. These evolution-based algorithms play a vital role in self-adapting the system under attack towards increasing and new types of attacks which are increasing day by day. One such area of soft computing algorithms investigated in this paper is the Artificial Neural Network or popularly known as ANNs. It works analogously to the biological neurons in the human body. In this paper, we systematically explain the ANN-based network model to counteract the DDoS attacks in the Multimedia Internet of Things, architecture, and implementation of ANNs, the experimental investigations and findings which help in drawing an inference of ANN-based defense models.