Abstract
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.
Original language | English |
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Title of host publication | 2016 International Symposium on Networks, Computers and Communications (ISNCC) |
Publisher | IEEE |
ISBN (Electronic) | 9781509002849 |
ISBN (Print) | 9781509002856 |
DOIs | |
Publication status | Published - 17 Nov 2016 |
Externally published | Yes |
Event | 2016 international Symposium on Networks, Computers and Communications : 2016 ISNCC - Yasmine Hammamet, Tunisia Duration: 11 May 2016 → 13 May 2016 |
Conference
Conference | 2016 international Symposium on Networks, Computers and Communications |
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Country/Territory | Tunisia |
City | Yasmine Hammamet |
Period | 11/05/16 → 13/05/16 |
Keywords
- Internet of things
- Denial of service
- Intrusion detection system
- Multi-level perceptron
- Artificial Neural Network