Intrusion Detection in Wireless Body Area Network using Attentive with Graphical Bidirectional Long-Short Term Memory

Authors

  • Shaymaa Adnan Abdulrahman Department of Computer Technology Engineering , College of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
  • Ehsan Qahtan Ahmed Computer Department- College of Science - AlNahrain University, Jadriya, Baghdad, Iraq
  • Zahraa A. Jaaz College of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Malaysia
  • Alya'a R. Ali Department of Public Relations, College of Media, Al-Farahidi University, Baghdad, Iraq

DOI:

https://doi.org/10.3991/ijoe.v19i06.37593

Keywords:

Wireless Body Area Network (WBAN); Healthcare; Intrusion Detection; Attention-based Bi-directional Long Short-Term Memory with Graph Construction (ABL-GC)

Abstract


Recently developed low-power networked systems, wireless communications, and wireless sensors have all contributed to the rise of Wireless Sensor Networks (WSNs) as a potentially useful tool in the medical field. Securing Wireless Body Area Networks (WBANs) is essential for their widespread use in healthcare environments because the data they send frequently includes private and confidential patient health information. The study's goal is to create a system for detecting intrusions in WBAN. To best identify attacks in such systems, we present a novel “Attention-based Bi-directional Long Short-Term Memory with Graph Construction” (ABL-GC) here. The suggested approach ensures that the intrusion detection system uses only the features essential to detect a given attack, reducing the processing complexity.

Cover iJOE Vol. 19 No. 06 (2023)

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Published

2023-05-16

How to Cite

Shaymaa Adnan Abdulrahman, Ehsan Qahtan Ahmed, Zahraa A. Jaaz, & Alya’a R. Ali. (2023). Intrusion Detection in Wireless Body Area Network using Attentive with Graphical Bidirectional Long-Short Term Memory. International Journal of Online and Biomedical Engineering (iJOE), 19(06), pp. 31–46. https://doi.org/10.3991/ijoe.v19i06.37593

Issue

Vol. 19 No. 06 (2023)

Section

Papers

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Copyright (c) 2023 Shaymaa Adnan Abdulrahman , Ehsan Qahtan Ahmed, Zahraa A. Jaaz, Alya'a R. Ali

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