A Deep learning approach for trust-untrust nodes classification problem in WBAN

Q1 Engineering

Periodicals of Engineering and Natural Sciences Pub Date : 2023-05-10 DOI:10.21533/pen.v11i3.3579

Hala Shaker Mehdy, Nariman Jabbar Qasim, Fadya A.Habeeb, Zinah S. Jabbar, Jamal Fadhil Tawfeq, Ahmed Dheyaa Radhi

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引用次数: 1

Abstract

The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use.

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WBAN中trust-untrust节点分类的深度学习方法

WBAN服务需求的巨大增长带来了一系列新的安全挑战。WBAN的能力正在发展以满足这些需求。移动环境的复杂性、异构性和不稳定性使其难以成功完成这些任务。使用信任-不信任节点分类可以获得更安全、更灵活的WBAN设置，这是满足WBAN安全需求的一种方法。考虑到这一点，我们提出了一种新的深度学习（DL）方法，用于使用基于空间注意力的迭代DBN（SA-IDBN）对WBAN节点进行分类。Z分数归一化用于从输入数据中去除重复条目。然后，采用线性判别分析（LDA）从归一化数据中提取特征。在准确性、延迟、回忆和f-measure方面，对所建议的方法的性能进行了检查，并与其他一些当前方法进行了对比。关于WBAN节点的分类，结果对所建议的方法比已经使用的方法更有利。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Periodicals of Engineering and Natural Sciences Engineering-Architecture

CiteScore

1.90

自引率

0.00%

发文量

140

审稿时长

7 weeks

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