CNN Intrusion Detection for Threat Analysis of a Network - Michael, Tressa (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The technological advancement realized in the discovery and embrace of both IoT and IIoT is totally indispensable. Many systems and subsystems both robust and miniaturized have made their existence into the technical arena due to IoT. It goes without saying that IoT has brought into light very diverse benefits that cut across universal applications.However, the pre-requisite of a network channel existence for an IoT operation to be successful is the only pitfall that this essentially unique system possesses. There is a significant amount of danger associated with transmission networks. They have very substantial susceptibility to both online and offline threats by malicious cyber intentions.This paper focuses on the analyses of the threats posed to these IoT networks through Artificial Neural Networks. Specifically, a model is trained through recurrent and convolutional neural network to do intensive analysis on the threat intensity, type and threat source for data logging purposes. The Intruder detection system (IDS) explored in this paper registers a success rate of 99% based on the empirical data posed to the model.