Secure Wireless Communication Using Support Vector Machines

Trung Q. Duong, Tiep M. Hoang, Sangarapillai Lambotharan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


We consider a wireless system consisting of $K$ legal users, one access point (AP)and one active eavesdropper. The eavesdropper is assumed to attack the system in the uplink phase. Focusing on intrusion detection, we introduce a framework to create datasets that are then put into support vector machine (SVM)classifiers. The characteristics of the three features (i.e., MEAN, RATIO and SUM)in our datasets are formulated from post-processing signals. Based on the three defined features, artificial training data (ATD)is also formed and used at the AP. By training SVM models, we show the high feasibility of detecting active eavesdroppers in many cases. The performance of our proposed approach is evaluated in terms of accuracy and through numerical examples.
Original languageEnglish
Title of host publication2019 IEEE Conference on Communications and Network Security (CNS): Workshops: Workshop on Physical-layer Methods for Security and Privacy in 5G and the IoT: Proceedings
Publisher IEEE
ISBN (Electronic) 978-1-5386-7117-7
ISBN (Print)978-1-5386-7118-4
Publication statusPublished - 19 Aug 2019
EventIEEE Conference on Communications and Network Security - Washington, D.C., United States
Duration: 10 Jun 201912 Jun 2019


ConferenceIEEE Conference on Communications and Network Security
CountryUnited States
CityWashington, D.C.

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