Exploiting CSI-MIMO for Accurate and Efficient Device Identification

Kandel, Laxima Niure; Zhang, Zhuosheng; Yu, Shucheng

doi:10.1109/globecom38437.2019.9014191

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…In addition, aiming at evaluating the performance and robustness of SEP at different SNR levels, we vary the SNR level from -10 dB to 25 dB with an increment of 5 dB, under different channels. The identification accuracy corresponding to different numbers of BSs at different SNR levels under the Rayleigh channel and time delay line (TDL) channel with TP rates of SEP proposed in this letter TP rates of Benchmark scheme in [7] TP rates of Full equalization proposed in [13] TN rates of SEP proposed in this letter TN rates of Benchmark scheme in [7] TN rates of Full equalization proposed in [13] Fig. 5.…”

Section: A Identification Performancementioning

confidence: 99%

“…Recognition accuracy versus SNR under different schemes. SEP with 64 antennas SEP with 32 antennas SEP with 16 antennas Benchmark in [7] with 64 antennas Benchmark in [7] with 32 antennas Benchmark in [7] with 16 antennas Fig. 6.…”

Section: A Identification Performancementioning

confidence: 99%

“…Consequently, the UE receives the pure DL signal without any feature, leading to a lack of features. Channel state information (CSI) is exploited in [7] for device identification using the training set and the test set with a ratio of 8.5:1.5. Inspired by this work, the benchmark scheme in the letter simulates 1,000 samples of CSI collected from different sources, from which these samples are first divided randomly into two parts: 80% of the training set and 20% of the test set.…”

Section: B Comparison With Other Schemesmentioning

confidence: 99%

“…The hardware part of the transmitting equipment and the receiving equipment has unique defects which can be used to identify the equipment on account of the uniqueness and robustness of radio frequency fingerprint (RFF) [6]. In addition, RFF of each device is also hard to tamper with [7]. So far, the research scope of the RFFI technology mainly covers ZigBee, LoRa, and Bluetooth.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Location-Invariant Radio Frequency Fingerprint for Base Station Recognition

Sun

Luo

et al. 2023

IEEE Wireless Commun. Lett.

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Pseudo base station (BS) is an illegal radio device that exploits the security vulnerabilities of fifth generation (5G) communications and then implements corresponding network attacks, such as spoofing attack. These malicious actions cause security and privacy threats and hinder the wide deployment of wireless access networks. To address these problems, efficient BS identification mechanisms are necessary. Radio frequency fingerprinting (RFF) and channel fingerprinting are potential solutions. Unfortunately, existing solutions are limited to be applicable to scenarios where the locations of pseudo BSs are fixed, while the emerging challenges brought by movable pseudo BSs can hardly be addressed by mature wireless security mechanisms. In this letter, we propose a signal echoing protocol (SEP) to reduce the influence on wireless channels and construct the locationinvariant RFF. When BS and user equipment (UE) communicate using SEP, UE can accurately estimate the channel to recognize different BSs. Numerical results demonstrate that the proposed scheme can reach a high classification accuracy for ten BSs, by 95.2% at the signal-noise ratio (SNR) of 25 dB.

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Section: A Identification Performancementioning

confidence: 99%

Section: A Identification Performancementioning

confidence: 99%

Section: B Comparison With Other Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Location-Invariant Radio Frequency Fingerprint for Base Station Recognition

Sun

Luo

et al. 2023

IEEE Wireless Commun. Lett.

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“…A number of scholars have conducted extensive research on the issue of RFF recognition [7][8][9][16][17][18][19][20][21], which is discussed below.…”

Section: Related Workmentioning

confidence: 99%

Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature

Shao

Wang³

et al. 2023

Electronics

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Internet of Things (IoT) technology is widely used in new power systems, and it also provides many new modes for network attacks. Illegal terminal device identification is also a significant topic in the field of wireless authentication technology. Some kinds of power network equipment are located in sparsely populated areas and rely on IoT terminals for real-time monitoring. Attackers use illegal terminals to connect power IoT devices for production monitoring and to carry out network attacks, which may cause serious damage, such as power data theft and misoperation of power network equipment. Radio frequency fingerprint (RFF) can extract hardware features from different devices, and is widely used for device identification and authentication. The area over which power network equipment placed is vast, and there are many wireless communication devices and terminals. It is difficult to identify illegal devices through commonly used network management techniques, thus making it difficult to distinguish between the mobile terminals of employees and illegal terminals in general spectrum screening. In response to the above situation, this paper uses the characteristics of the squared spectrum of random access preamble signals to extract hardware device features, proposes an illegal device identification algorithm based on Gaussian distribution theory, and evaluates its performance. The experimental results show that, when the signal-to-noise ratio (SNR) is greater than 15 dB, the average recognition result is greater than 92%. In addition, the algorithm has low computational complexity and high engineering application value.

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Privacy-Preserving Authentication in Wireless Access Networks

Yu¹,

Lou²

2022

Encyclopedia of Cryptography, Security and Privacy

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Exploiting CSI-MIMO for Accurate and Efficient Device Identification

Cited by 8 publications

References 15 publications

Location-Invariant Radio Frequency Fingerprint for Base Station Recognition

Location-Invariant Radio Frequency Fingerprint for Base Station Recognition

Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature

Privacy-Preserving Authentication in Wireless Access Networks

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