A Lightweight Intelligent Authentication Approach for Intrusion Detection

被引：0
|
作者：
Qiu, Xiaoying [1 ]
Li, Zhidu [2 ]
Sun, Xuan [1 ]
Xu, Tongyang [3 ]
机构：
[1] Beijing Informat Sci & Technol Univ, Sch Informat Management, Beijing 100192, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China
[3] UCL, Dept Elect & Elect Engn, London, England
来源：
2020 IEEE 31ST ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (IEEE PIMRC) | 2020年
关键词：
Physical layer authentication; artificial intelligence; deep learning; CNN; classification; prototyping; software defined radio; WIRELESS NETWORKS; CHALLENGES;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页数：6
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