Physical Layer Key Generation in 5G Wireless Networks

Jiao, Liudan; Wang, Ning; Wang, Pu; Alipour-Fanid, Amir; Tang, Jie; Zeng, Kai

doi:10.1109/mwc.001.1900061

Cited by 87 publications

(46 citation statements)

References 15 publications

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“…Fig. 10(a) shows the channel differences between the clone node and the legitimate node after normalization ranging from 0 to 1 according to (8). This figure shows that the channel difference of the clone node is greater than that of the legitimate node.…”

Section: A Experimental Setup For Verificationmentioning

confidence: 99%

“…Especially for clone attacks, as attackers are able to obtain all sensitive information from a compromised legitimate node, it is difficult to detect and deter these attacks by relying on cryptographic algorithms. In [2]- [8], physical layer authentication based on generalized channel responses with spatial variability was put forward, which is based on generalized channel response with spatial variability and considers the correlation of time, frequency, and spatial domains. On account of this, we propose an attack detection method based on channel differences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Labeling and Learning for Physical Layer Authentication Against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks

Chen

Pang

Wen

et al. 2021

IEEE Trans. Ind. Inf.

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In this article, a scheme to detect both clone and Sybil attacks by using channel-based machine learning is proposed. To identify malicious attacks, channel responses between sensor peers have been explored as a form of fingerprints with spatial and temporal uniqueness. Moreover, the machine-learning-based method is applied to provide a more accurate authentication rate. Specifically, by combining with edge devices, we apply a threshold detection method based on channel differences to provide offline training sample sets with labels for the machine learning algorithm, which avoids manually generating labels. Therefore, our proposed scheme is lightweight for resource constrained industrial wireless devices, since only an online-decision making is required. Extensive simulations and experiments were conducted in real industrial environments. Both results show that the authentication accuracy rate of our strategy with an appropriate threshold can achieve 84% without manual labeling.

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Section: A Experimental Setup For Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Labeling and Learning for Physical Layer Authentication Against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks

Chen

Pang

Wen

et al. 2021

IEEE Trans. Ind. Inf.

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“…Jiao, Long, Ning Wang Et al. [21] The blossom of the 5G correspondence and past fills in as an impetus for actual layer key age strategies. In 5G interchanges frameworks, numerous difficulties in conventional actual layer key age plans, for example, co-found busybodies, the high piece contradiction proportion, and high worldly relationship, could be survived.…”

Section: Related Workmentioning

confidence: 99%

“…Instead of asymmetric cryptography is also called public cryptography, this method applied two different keys for the process of encryption of description. Some other cryptography algorithms is based on the combination of public and private key cryptography [21,22].…”

Section: Cryptography and Key Generation Approachmentioning

confidence: 99%

Analysis of Key Generation Methods for Enable Secured Communication in the IoTs System

Patankar¹

2021

ITII

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The open stack communication protocol of the internet of things always in security threats. The vulnerability of protocol faces a problem of man in middle attacks and loss of integrity of communication. Cryptography plays a vital role in security authentication on IoT based enable communication system. IoT devices' limited resource constraints cannot offer any security mechanism on the physical level and network level. The key generation methods uplift the security provision in IoT enables communication model. The process of key generation applied three types of cryptography algorithm, symmetric, asymmetric and hybrid algorithm. The complexity of key generation methods needs more computational time and memory; this is the limitation of IoT devices. So various authors proposed a lightweight key generation algorithm for security and authentication. This paper analyzed the performance of key generation methods based on different cryptography approaches and measured their security strength. For the analysis process, use MATLAB software and sample text data file for the communication of two parties. Some ADHOC methods apply to measure protocol weakness during the communication network compromised with a man in the middle attack. The variable key size also reflects the security strength of IoTs enable devices.

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“…Key generation based on the channel reciprocity [11] can effectively alleviate the problems of key distribution and management. Unfortunately, channel-based key generation is susceptible to channel noise, which will result in a high bit disagreement ratio under low signal-to-noise ratio (SNR) [12], [13]. The inability to obtain a consistent shared secret key is intolerable to the authentication mechanisms, which utilize the hash functions to generate authentication codes.…”

Section: Introductionmentioning

confidence: 99%

A Tag-Based PHY-Layer Authentication Scheme Without Key Distribution

Zhang

2021

IEEE Access

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Authentication is the process of confirming the legal identity of communicating entities, and it is the first line of defense for security communication. Most of the existing tag-based physical layer security authentication (PLSA) requires distributing the shared keys in advance. In the large scale internet of things scenario, nodes frequently join and leave the wireless networks that cause the distribution and management of keys particularly difficult. This paper proposes a tag-based PLSA scheme, which utilizes channel characteristics instead of distributing keys to generate authentication tags. Specifically, based on watermarking mechanism, we design a fault-tolerant hash algorithm to couple the secret sequence and the message signal for authentication tags generation. The shared secret sequence is generated by legitimate nodes through channel probing. And the theories of information theory and composite hypothesis testing is employed to analyze the performance of system. The simulation results show that the agreement ratio of the generating shared secret sequence is as high as 96% in the case of high signal-to-noise ratio and low power tag embedding. In addition, performance analysis demonstrates the scheme can resist against multiple attacks, such as replay, jamming, tampering, and impersonation attack.INDEX TERMS Authentication, wireless network, tag-based, hash algorithm, channel probing.

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Physical Layer Key Generation in 5G Wireless Networks

Cited by 87 publications

References 15 publications

Automated Labeling and Learning for Physical Layer Authentication Against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks

Automated Labeling and Learning for Physical Layer Authentication Against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks

Analysis of Key Generation Methods for Enable Secured Communication in the IoTs System

A Tag-Based PHY-Layer Authentication Scheme Without Key Distribution

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