Back to Search
Start Over
CNN Intrusion Detection for Threat Analysis of a Network
- Source :
- Turkish Journal of Computer and Mathematics Education (TURCOMAT). 12:3945-3949
- Publication Year :
- 2021
- Publisher :
- Auricle Technologies, Pvt., Ltd., 2021.
-
Abstract
- 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.
- Subjects :
- Online and offline
Empirical data
Channel network
Artificial neural network
Computer science
General Mathematics
Intrusion detection system
Computer security
computer.software_genre
Convolutional neural network
Education
Computational Mathematics
Computational Theory and Mathematics
Transmission (telecommunications)
Data logger
computer
Subjects
Details
- ISSN :
- 13094653
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Turkish Journal of Computer and Mathematics Education (TURCOMAT)
- Accession number :
- edsair.doi...........71bca0d658cb9332344c1c217fae0aa5