Routing Protocol Attack Detection Using Machine Learning Through Parallel Computing in Wireless Sensor Network

The wireless sensor network is a hot and significant research area nowadays and it can be addressed in almost every sector and environment. A few major challenges are countered in the wireless sensor network such as energy consumption, battery lifetime, Attacks, Data Transmission, etc. Generally wireless sensor network produces non-Euclidian sensing data and metadata structures and it is very complex to deal with the structure, especially in order to measure anomalies and disruption in a network. In this paper, we have introduced parallel computing to resolve the heterogeneity of the sensing data and metadata as well. Parallel computing has been applied implicitly for extracting only paramount data from the large scale of data to detect routing layer attacks. The convolution neural network (CNN) has been considered as a machine-learning model and we have enhanced the kernel to optimize the performance of the conventional CNN model to detect network layer attacks in terms of wireless sensor networks. Our investigated method demonstrates better results in detecting anomalies and attacks than the existing methods or techniques.