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

Nyangaresi, Vincent Omollo; Ahmad, Musheer; Alkhayyat, Ahmed; Feng, Wei

doi:10.1111/exsy.13126

Cited by 42 publications

(36 citation statements)

References 73 publications

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“…However, this protocol is susceptible to modification, replay, MitM and impersonation attacks. The multi-party access mechanism by Zhang et al [32] also incurs high processing overheads [33] when large numbers of IoT devices are deployed. This problem can be addressed by the protocol developed by Ali et al [34] , which is shown to have less computation overheads and high throughputs.…”

Section: Related Workmentioning

confidence: 99%

The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

Nyakomitta

Ogara

Abounji

2022

j. of Comput. sci. res.

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The Internet of Things devices and users exchange massive amount of data. Some of these exchanged messages are highly sensitive as they involve organizational, military or patient personally identifiable information. Therefore, many schemes and protocols have been put forward to protect the transmitted messages. The techniques deployed in these schemes may include blockchain, public key infrastructure, elliptic curve cryptography, physically unclonable function and radio frequency identification. In this paper, a review is provided of these schemes including their strengths and weaknesses. Based on the obtained results, it is clear that majority of these protocols have numerous security, performance and privacy issues.

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Section: Related Workmentioning

confidence: 99%

The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

Nyakomitta

Ogara

Abounji

2022

j. of Comput. sci. res.

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“…The prediction of cancer at early stages is essential as it can simplify the subsequent clinical requirements of patients and determining the effective treatment [12] . Various techniques such as clinical (traditional) techniques [21] and machine learning algorithms [22] have been proposed by researchers for early detection and prediction of breast cancer.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, quantum nearest mean classifier has utilized the binary classification where quantum version is employed to estimate the optimal performance measures [43] . Early studies in the area of QML by Zhang and Ni [22] have shown that QML is one of the most promising technologies of research in the future. The experimental demonstration in the field of quantum computing has made remarkable interests in the recent years, giving rise to new striking possibilities of enhancing machine learning with quantum devices [53] .…”

Section: Consumes Timementioning

confidence: 99%

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Arika

Mindila

Cheruiyo

2022

Jour. of oncol. res.

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Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.

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“…The proposed model is validated using the benchmark dataset and the results are examined in terms of precision, recall, accuracy, and F1-score. Nyangaresi et al, 2022 deploy an ANN for target cell selection in a 5G network, and authentication of communicating entities takes place before being admitted in the new cell. It has a low packet loss ratio and latency variations.…”

mentioning

confidence: 99%

“…Nyangaresi et al, 2022 deploy an ANN for target cell selection in a 5G network, and authentication of communicating entities takes place before being admitted in the new cell. It has a low packet loss ratio and latency variations.…”

mentioning

confidence: 99%

Special issue on International Conference on Computing and Communication Networks

Gupta

2022

Expert Systems

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

Cited by 42 publications

References 73 publications

The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Special issue on International Conference on Computing and Communication Networks

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