ANN-FL Secure Handover Protocol for 5G and Beyond Networks

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

doi:10.1007/978-3-030-70572-5_7

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…For instance, the demand for short response times in real-time services is the most challenging aspect in IoMT. Here, machine learning algorithms can be applied for handoff optimization as well as target cell selection to minimize handover latencies [97]- [106]. In this way, the quality of service (QoS) of the offered remote access services will be enhanced.…”

Section: Artificial Intelligent (Ai)mentioning

confidence: 99%

Internet of medical things: Prospects, challenges and future research directions

Isiaho¹,

Omieno²

2022

Global J. Eng. Technol. Adv.

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The internet of medical things has been developed to facilitate remote monitoring of the patients as well as the elderly. Unfortunately, the communication between the remote patients and the medical doctors is the open wireless channels. Therefore, the data transmitted can be eavesdropped, intercepted, replayed or modified. This poses serious challenges to patient privacy as well as endangering patient life. To curb this, numerous schemes have been put forward by various researchers over the recent past. In this paper, we provide an extensive review of these schemes in an effort to identify any gaps. Consequently, we show that the current security and privacy preservation schemes have many challenges that render the communication process insecure or inefficient. As such, we offer some suggestions for the requirements of an ideal security technique that will not only be efficient but also provably secure.

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Section: Artificial Intelligent (Ai)mentioning

confidence: 99%

Internet of medical things: Prospects, challenges and future research directions

Isiaho¹,

Omieno²

2022

Global J. Eng. Technol. Adv.

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“…Consequently, the Third-Generation Partnership Project (3GPP) has standardized three Authentication and Key Agreement (AKA) protocols for 5G, which include 5G AKA, Extensible Authentication Protocol AKA (EAP-AKA) and EAP Transport Layer Security (EAP-TLS). However, these protocols are still susceptible to numerous attacks such as Denial of Service (DoS), man-in-the-middle (MitM) and impersonation (Borgaonkar et al, 2019;Nyangaresi et al, 2020b). In addition, these schemes are inappropriate for the management of massive devices such as those present in IoT (Ouaissa et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and symmetric key cryptography based verification protocol for 5G enabled Internet of Things

Nyangaresi¹,

Ahmad

Alkhayyat

et al. 2022

Expert Systems

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“…FL based estimation technique is presented in [10] , while authors in [11] have developed a neural network system for predicting the number of users in a network. To boost quality of service (QoS) in 5G networks, authors in [12] present ANN-FL scheme. Similarly, ANN algorithm is deployed in [13] for QoS and Quality of Experience (QoE) enhancement.…”

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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ANN-FL Secure Handover Protocol for 5G and Beyond Networks

Cited by 17 publications

References 15 publications

Internet of medical things: Prospects, challenges and future research directions

Internet of medical things: Prospects, challenges and future research directions

Artificial neural network and symmetric key cryptography based verification protocol for 5G enabled Internet of Things

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

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