Physical Layer Authentication Enhancement Using a Gaussian Mixture Model
Wireless networks strive to integrate information technology into every corner of the world. This openness of radio propagation is one reason why holistic wireless security mechanisms only rarely enter the picture. In this paper, we propose a physical (PHY)-layer security authentication scheme that...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8468974/ |
| _version_ | 1818618839545413632 |
|---|---|
| author | Xiaoying Qiu Ting Jiang Sheng Wu Monson Hayes |
| author_facet | Xiaoying Qiu Ting Jiang Sheng Wu Monson Hayes |
| author_sort | Xiaoying Qiu |
| collection | DOAJ |
| description | Wireless networks strive to integrate information technology into every corner of the world. This openness of radio propagation is one reason why holistic wireless security mechanisms only rarely enter the picture. In this paper, we propose a physical (PHY)-layer security authentication scheme that takes advantage of channel randomness to detect spoofing attacks in wireless networks. Unlike most existing authentication techniques that rely on comparing message information between the legitimate user and potential spoofer, our proposed authentication scheme uses a Gaussian mixture model (GMM) to detect spoofing attackers. Probabilistic models of different transmitters are used to cluster messages. Furthermore, a 2-D feature measure space is exploited to preprocess the channel information. Training data for a spoofer operating through an unknown channel, a pseudo adversary model is developed to enhance the spoofing detection performance. Monte Carlo simulations are used to evaluate the detection performance of the GMM-based PHY-layer authentication scheme. The results show that the probability of detecting a spoofer is higher than that obtained using similar approaches. |
| first_indexed | 2024-12-16T17:27:58Z |
| format | Article |
| id | doaj.art-3cfadc69c37a409b840515a865808359 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:27:58Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3cfadc69c37a409b840515a8658083592022-12-21T22:23:00ZengIEEEIEEE Access2169-35362018-01-016535835359210.1109/ACCESS.2018.28715148468974Physical Layer Authentication Enhancement Using a Gaussian Mixture ModelXiaoying Qiu0https://orcid.org/0000-0002-5585-5220Ting Jiang1Sheng Wu2Monson Hayes3Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USAWireless networks strive to integrate information technology into every corner of the world. This openness of radio propagation is one reason why holistic wireless security mechanisms only rarely enter the picture. In this paper, we propose a physical (PHY)-layer security authentication scheme that takes advantage of channel randomness to detect spoofing attacks in wireless networks. Unlike most existing authentication techniques that rely on comparing message information between the legitimate user and potential spoofer, our proposed authentication scheme uses a Gaussian mixture model (GMM) to detect spoofing attackers. Probabilistic models of different transmitters are used to cluster messages. Furthermore, a 2-D feature measure space is exploited to preprocess the channel information. Training data for a spoofer operating through an unknown channel, a pseudo adversary model is developed to enhance the spoofing detection performance. Monte Carlo simulations are used to evaluate the detection performance of the GMM-based PHY-layer authentication scheme. The results show that the probability of detecting a spoofer is higher than that obtained using similar approaches.https://ieeexplore.ieee.org/document/8468974/PHY-layer authenticationGaussian mixture modelspoofing detectionwireless security |
| spellingShingle | Xiaoying Qiu Ting Jiang Sheng Wu Monson Hayes Physical Layer Authentication Enhancement Using a Gaussian Mixture Model IEEE Access PHY-layer authentication Gaussian mixture model spoofing detection wireless security |
| title | Physical Layer Authentication Enhancement Using a Gaussian Mixture Model |
| title_full | Physical Layer Authentication Enhancement Using a Gaussian Mixture Model |
| title_fullStr | Physical Layer Authentication Enhancement Using a Gaussian Mixture Model |
| title_full_unstemmed | Physical Layer Authentication Enhancement Using a Gaussian Mixture Model |
| title_short | Physical Layer Authentication Enhancement Using a Gaussian Mixture Model |
| title_sort | physical layer authentication enhancement using a gaussian mixture model |
| topic | PHY-layer authentication Gaussian mixture model spoofing detection wireless security |
| url | https://ieeexplore.ieee.org/document/8468974/ |
| work_keys_str_mv | AT xiaoyingqiu physicallayerauthenticationenhancementusingagaussianmixturemodel AT tingjiang physicallayerauthenticationenhancementusingagaussianmixturemodel AT shengwu physicallayerauthenticationenhancementusingagaussianmixturemodel AT monsonhayes physicallayerauthenticationenhancementusingagaussianmixturemodel |