Back Propagation Algorithm-Based Intelligent Model for Botnet Detection

The ever-increasing growth of network computers and the internet of things makes botnet recognition become more difficult and it is making it all the more less difficult for intruders and attackers to propagate botnet infections. The unified propagation character of botnet floods warms through different botnet environment and clients the network security. To conquer the down sides in determining the botnet, we propose a back-propagation algorithm for botnet recognition. The focus of this study is a proposed back propagation algorithm in for training the sensor leveraging the machine learning techniques, which will keep an eye on attributes of the identified or recognized traffic flow. For every identified attribute recognized, it quickly identifies, which will include nine attributes used and identify it altogether. Every time the traffic is determined, its flow is tracked and weighed against the set of attributes within the feature set for the event of address within the network route.