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

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.