Qiu, X;
Lit, Z;
Sun, X;
Xu, T;
(2020)
A lightweight intelligent authentication approach for intrusion detection.
In:
Proceedings of 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications.
IEEE: London, UK.
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Abstract
Internet of things (IoT) offers advanced and intelligent services for our life. However, smart IoT devices also bring various security vulnerabilities. Traditionally, attacks are solved by conventional authentication and authorization schemes, requiring extensive time and computational resources. In addition, it is possible to exploit artificial intelligence (AI) to provide countermeasures while enabling lightweight authentication. In this paper, we explore a solution on modelling a spoofing detection system based on machine learning and we propose a deep learning method using Auto-Extractor/Classifier Neural Network. Our scheme operates on the physical layer without causing computational overhead. Therefore, the lightweight authentication can be achieved and spoofing attacks are well- controlled in IoT scenarios.
Type: | Proceedings paper |
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Title: | A lightweight intelligent authentication approach for intrusion detection |
Event: | 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications |
ISBN-13: | 9781728144900 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/PIMRC48278.2020.9217112 |
Publisher version: | https://doi.org/10.1109/PIMRC48278.2020.9217112 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Physical layer authentication, artificial intelligence, deep learning, CNN, classification, prototyping, software defined radio. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10117008 |



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