Neuro-Fuzzy Based Handover Authentication Protocol for Ultra Dense 5G Networks

Nyangaresi, Vincent Omollo; Rodrigues, Anthony; Abeka, Silvance O.

doi:10.1109/gpecom49333.2020.9247938

Cited by 37 publications

(20 citation statements)

References 21 publications

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“…A protocol is built in [17] to mitigate the vulnerabilities which make it susceptible to jamming, DoS attacks, missing key confirmatory attacks, traceability attacks, and replay attacks. Any such attacks were prevented by the developing specification; the use of that protocol is clear since it maintains the traditional 5G architecture.…”

Section: Handover Operationmentioning

confidence: 99%

Technical Study on 5G Using Soft Computing Methods

Divakaran¹,

Malipatil²,

Zaid³

et al. 2022

Scientific Programming

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With increasing advancements in the field of telecommunication, the attainment of a higher data transfer rate is essentially a greater need to meet high-performance communication. The exploitation of the fuzzy system in the wireless telecommunication systems, especially in Fifth Generation Mobile Networks (or) 5G networks is a vital paradigm in telecommunication markets. A comprehensive survey is dealt in the paper, where it initially reviews the basic understanding of fuzzy systems over 5G telecommunication. The literature studies are collected from various repositories that include reference materials, Internet, and other books. The collection of articles is based on empirical or evidence-based from various peer-reviewed journals, conference proceedings, dissertations, and theses. Most of the existing soft computing models are streamlined to certain applications of 5G networking. Firstly, it is hence essential to provide the readers to find research gaps and new innovative models on wide varied applications of 5G. Secondly, it deals with the scenarios in which the fuzzy systems are developed under the 5G platform. Thirdly, it discusses the applicability of fuzzy logic systems on various 5G telecommunication applications. Finally, the paper derives the conclusions associated with various studies on the fuzzy systems that have been utilized for the improvement of 5G telecommunication systems.

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Section: Handover Operationmentioning

confidence: 99%

Technical Study on 5G Using Soft Computing Methods

Divakaran¹,

Malipatil²,

Zaid³

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Authors in [25][26][27] have introduced bilinear pairing based mutual authentication schemes, but which results in excessive computational overheads [28] . On the other hand, the smart card based biometric authentication algorithm in [29] cannot provide anonymity and is vulnerable to impersonation attacks [15] .…”

Section: Related Workmentioning

confidence: 99%

Efficient Authentication Algorithm for Secure Remote Access in Wireless Sensor Networks

Nyakomitta¹,

Nyangaresi²,

Ogara

2021

j. of Comput. sci. res.

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Wireless sensor networks convey mission critical data that calls for adequate privacy and security protection. To accomplish this objective, numerous intrusion detection schemes based on machine learning approaches have been developed. In addition, authentication and key agreements techniques have been developed using techniques such as elliptic curve cryptography, bilinear pairing operations, biometrics, fuzzy verifier and Rabin cryptosystems. However, these schemes have either high false positive rates, high communication, computation, storage or energy requirements, all of which are not ideal for battery powered sensor nodes. Moreover, majority of these algorithms still have some security and privacy challenges that render them susceptible to various threats. In this paper, a WSN authentication algorithm is presented that is shown to be robust against legacy WSN privacy and security attacks such as sidechannel, traceability, offline guessing, replay and impersonations. From a performance perspective, the proposed algorithm requires the least computation overheads and average computation costs among its peers.

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“…On the other hand, a multilayer perception neural network (MPNN) is presented in [17] for delay reduction. Similarly, a neuro-fuzzy based technique is developed in [18] for QoS optimization.…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

Nyangaresi¹,

El-Omari²,

Nyakina³

2022

j. of Comput. sci. res.

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Machine learning algorithms have been deployed in numerous optimization, prediction and classification problems. This has endeared them for application in fields such as computer networks and medical diagnosis. Although these machine learning algorithms achieve convincing results in these fields, they face numerous challenges when deployed on imbalanced dataset. Consequently, these algorithms are often biased towards majority class, hence unable to generalize the learning process. In addition, they are unable to effectively deal with high-dimensional datasets. Moreover, the utilization of conventional feature selection techniques from a dataset based on attribute significance render them ineffective for majority of the diagnosis applications. In this paper, feature selection is executed using the more effective Neighbour Components Analysis (NCA). During the classification process, an ensemble classifier comprising of K-Nearest Neighbours (KNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) is built, trained and tested. Finally, cross validation is carried out to evaluate the developed ensemble model. The results shows that the proposed classifier has the best performance in terms of precision, recall, F-measure and classification accuracy.

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Neuro-Fuzzy Based Handover Authentication Protocol for Ultra Dense 5G Networks

Cited by 37 publications

References 21 publications

Technical Study on 5G Using Soft Computing Methods

Technical Study on 5G Using Soft Computing Methods

Efficient Authentication Algorithm for Secure Remote Access in Wireless Sensor Networks

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

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