Pseudonym Mutable Based Privacy for 5G User Identity

Saeed, Rashid A.; Saeed, Mamoon M.; Mokhtar, Rania A.; Alhumyani, Hesham; Abdel‐Khalek, S.

doi:10.32604/csse.2021.015593

Cited by 19 publications

(7 citation statements)

References 29 publications

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“…Many studies proposed solutions for location privacy: Refs. [87] and [86] proposed a reduction of the positioning quality data to preserve location privacy. Techniques such as obfuscation are also crucial.…”

Section: The Proposed Solutions For Privacy In 5g Network and Discussionmentioning

confidence: 99%

“…The authors discussed mobile users' security issues in SDN, NFV, and clouds [12,87]. Moreover, they believed that there must be shared arrangements and reliance replicas among several participants complicated in the processes; for instance, application designers, networks operators, users, ser-vices providers, and an industrialist on information use and storing to sanctuary the privacy of users in 5G system.…”

Section: 13mentioning

confidence: 99%

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A comprehensive review on the users’ identity privacy for 5G networks

et al. 2022

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Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. Therefore, researchers have deliberated the privacy and security solution of 5G users. The 5G wireless network offers a futuristic concept that helps to solve challenges affecting previous communications generations. The key concern to many scholars in the field of mobile networking is user privacy, which is long‐term subscription identifier as International Mobiles Subscribers Identifiers (IMSIs) and short‐term subscription identifier as Temporary Mobiles Subscribers Identifiers and Cell‐Radio Networks Temporary Identifiers (TMSIs and C‐RNTIs), which are used for permanent identifying, paging, and location update. This article investigates the existing literature survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies that handle user identifications in authentication, paging, and location update. This article discusses the various privacy issues in the 5G network that use IMSI in clear text or temporary identities such as TMSI & C‐RNTI with IMSI to disclose user identity privacy. This article also investigates the existing literature on user identity and location privacy and highlights the key parameters, issues, challenges, and future recommendations with potential solutions.

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Section: The Proposed Solutions For Privacy In 5g Network and Discussionmentioning

confidence: 99%

Section: 13mentioning

confidence: 99%

A comprehensive review on the users’ identity privacy for 5G networks

et al. 2022

View full text Add to dashboard Cite

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“…The proposed study evaluates the use of semi-supervised learning and unsupervised learning techniques to detect anomalies. The authors used the K-means clustering approach and training instances through the Euclidean distance method and then evaluated the C4.5 algorithm [32]. Their results showed that the semi-supervised training algorithm gives better performance than supervised or unsupervised algorithms.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

et al. 2022

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Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.

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“…The 5G mobile network would inspire the IT and CT integration to discover more new services and applications to allow all industries types to contribute to the transformation toward digital [32][33]. The customization and openness abilities will growth in the next mobile generation, with more interfaces for API that support agile and flexible businesses for customers to qualify and fulfil the deploying requirement of tailored customized and networks services [34].…”

Section: Openness and Customizationmentioning

confidence: 99%

Green communication in 6G

Hoque¹,

Farhad²,

Mokhtar³

et al. 2022

IET Conf. Proc.

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Green communication networking is a part of sustainable development. It aims to reduce energy consumption and serve the network to a vast number of servers cost-effectively. Green communication integrates artificial intelligence to solve complex mathematical problems for enhancing energy efficiency, flexibility, security, and quality of life services. One of the most remarkable characteristics of today's telecoms sector is wireless communication, used for more than a century. Infrared, radio frequency, satellite, and other electromagnetic waves are used in wireless communication technologies to send data over the air. More data is necessary for the 6G experience. More data is necessary and more excellent ambient sensing and awareness. Sensing systems in automated cars, for example, are exceedingly complicated and rely on cellular networks. Mobile and wireless networks, such as 6G, can use green communication to balance resource usage and conserve energy. In this study, 6G for green communication has been discussed to justify the necessity of 6G.

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Pseudonym Mutable Based Privacy for 5G User Identity

Cited by 19 publications

References 29 publications

A comprehensive review on the users’ identity privacy for 5G networks

A comprehensive review on the users’ identity privacy for 5G networks

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

Green communication in 6G

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