A Novel Physical Layer Authentication Method with Convolutional Neural Network
- Resource Type
- Conference
- Authors
- Liao, Runfa; Wen, Hong; Pan, Fei; Song, Huanhuan; Xu, Aidong; Jiang, Yixin
- Source
- 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Artificial Intelligence and Computer Applications (ICAICA), 2019 IEEE International Conference on. :231-235 Mar, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Authentication
Convolution
Convolutional neural networks
Wireless communication
Radio transmitters
OFDM
channel authentication
multi-user
physical layer security
MIMO-OFDM
convolutional neural network
- Language
This paper investigates the physical layer (PHY-layer) authentication that exploits channel state information (CSI) to enhance multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system security by detecting spoofing attacks in wireless networks. A multi-user authentication system is proposed using convolutional neural networks (CNNs) which also can distinguish spoofers effectively. In addition, the mini batch scheme is used to train the neural networks and accelerate the training speed. Meanwhile, L1 regularization is adopted to prevent over-fitting and improve the authentication accuracy. The convolutional-neural-network-based (CNN-based) approach can authenticate legitimate users and detect attackers by CSIs with higher performances comparing to traditional hypothesis test based methods.