scholarly journals A LoRa-Based Lightweight Secure Access Enhancement System

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yu Jiang ◽  
Hua Fu ◽  
Aiqun Hu ◽  
Wen Sun
Keyword(s):  
Control Mechanism ◽  
Physical Layer ◽  
Security Risks ◽  
Access Technology ◽  
Security Enhancement ◽  
Secure Access ◽  
Single Frame ◽  
Power Spectral ◽  
Authentication System ◽  
Authentication Security

The access control mechanism in LoRa has been proven to have high security risks. In order to improve the secure access ability of LoRa terminals, this paper presents a physical layer-based authentication system for security enhancement. Different from the security access technology of cryptography, a lightweight gateway architecture called LW-LoRaWAN is proposed to realize a data frame-based authentication with radio frequency fingerprint (RFF). A novel RFF feature of Cross Power Spectral Density (CPSD) is used to achieve a fast authentication with one single frame. Theoretical analysis and experimental results show that the proposed system not only reinforces the authentication security of LoRa network but also protects the LoRa terminals against the Sybil attacks. The LW-LoRaWAN provides new security approach from physical layer for LoRa network.

scholarly journals Secure Physical Layer Transmission and Authentication Mechanism Based on Compressed Sensing of Multiple Antenna Arrays

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaolong Zhang ◽  
Wei Wu ◽  
Bin Zhou
Keyword(s):  
Antenna Arrays ◽  
High Speed ◽  
Large Scale ◽  
Physical Layer ◽  
Security Enhancement ◽  
Authentication Mechanism ◽  
Data Authentication ◽  
Enhancement Method ◽  
Authentication Security ◽  
Lightweight Authentication

Large-scale antenna technology has become one of the most promising technologies in 5G because of its ability to effectively improve the spectral efficiency and energy efficiency of the system, as well as its better robustness. In this paper, a large amount of CSI (channel state information) data is characterized by feature mining and law analysis, and a large number of channel characteristics of the physical layer have the advantages of randomness and uniqueness, etc. From the perspective of improving the security of the authentication mechanism and reducing the computational complexity of the authentication mechanism, the physical layer security authentication model is analyzed, and the signal security transmission path, signal authenticity, and channel estimation are used to propose an effective physical layer secure transmission and authentication mechanism, which can be used as a security enhancement and lightweight authentication mechanism for existing authentication mechanisms. In this paper, we analyze the security advantage mechanism of physical layer authentication and prove the security performance boundary; propose an authentication security enhancement method based on the channel feature generation key, a message authentication method based on superimposed tag signals; and propose a method based on private guide frequency for high-speed service data authentication.

scholarly journals An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1664 ◽  
Author(s):  
Haiping Huang ◽  
Linkang Hu ◽  
Fu Xiao ◽  
Anming Du ◽  
Ning Ye ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Reference Electrode ◽  
Identity Authentication ◽  
Classification Model ◽  
Experimental Result ◽  
Ensemble Averaging ◽  
Security Risks ◽  
Adaptive Feature Selection ◽  
Authentication System ◽  
Audiovisual Presentation

With the continuous increment of security risks and the limitations of traditional modes, it is necessary to design a universal and trustworthy identity authentication system for intelligent Internet of Things (IoT) applications such as an intelligent entrance guard. The characteristics of EEG (electroencephalography) have gained the confidence of researchers due to its uniqueness, stability, and universality. However, the limited usability of the experimental paradigm and the unsatisfactory classification accuracy have so far prevented the identity authentication system based on EEG to become commonplace in IoT scenarios. To address these problems, an audiovisual presentation paradigm is proposed to record the EEG signals of subjects. In the pre-processing stage, the reference electrode, ensemble averaging, and independent component analysis methods are used to remove artifacts. In the feature extraction stage, adaptive feature selection and bagging ensemble learning algorithms establish the optimal classification model. The experimental result shows that our proposal achieves the best classification accuracy when compared with other paradigms and typical EEG-based authentication methods, and the test evaluation on a login scenario is designed to further demonstrate that the proposed system is feasible, effective, and reliable.

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