Threat analysis of IoT networks using artificial neural network intrusion detection system
| Contribuinte(s) |
Abertay University. School of Arts Media & Computer Games |
|---|---|
| Data(s) |
24/10/2016
24/10/2016
14/05/2016
|
| Resumo |
The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks. |
| Identificador |
Hodo, E. et al. 2016. Threat analysis of IoT networks using artificial neural network intrusion detection system. Paper presented at International Symposium on Networks, Computers and Communications, Hammamet, Tunisia. 9781509002849 |
| Idioma(s) |
en |
| Direitos |
This is the accepted manuscript, which is due to be published, © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Palavras-Chave | #Internet of things #Artificial Neural Network #Denial of service #Intrusion detection system #Multi-level perceptron |
| Tipo |
Conference Paper unpublished n/a accepted |
