Robust Jamming Attacks Detection Algorithm for Healthcare Applications

Due to the growth of Wireless Body Sensor Networks (WBSN), smart wearable sensors have emerged that support efficient healthcare solutions. WBSNs are commonly used in unattended hostile environments. Indeed, they are vulnerable to various security threats. The jamming attack, in which the attacker u...

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Bibliographic Details
Main Authors: Belhaj Mohamed, Mbarka, Meddeb-Makhlouf, Amel, Fakhfakh, Ahmed, Kanoun, Olfa
Format: Conference Proceeding
Language:English
Subjects:
ECG signal
Electrocardiography
Jamming attacks
Medical services
Physiology
Received signal strength indicator
RSSI
Security
Simulation
Throughput
WBSN
Wireless communication
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Summary:Due to the growth of Wireless Body Sensor Networks (WBSN), smart wearable sensors have emerged that support efficient healthcare solutions. WBSNs are commonly used in unattended hostile environments. Indeed, they are vulnerable to various security threats. The jamming attack, in which the attacker uses the same frequency signals to jam the network transmission, is one of the most popular types of vulnerability threat. In this paper, a jamming attack detection algorithm is proposed, where four types are distinguished, which are constant jamming, deceptive jamming, random jamming, and reactive jamming experienced in WBSNs. The advisor aims to disturb the normal operation of nodes by sending random signals through the network, which severely affect network throughput, latency, and energy consumption. Our research provides insight into ElectroCardioGram (ECG) physiological signals. For the indication of a jamming attack in the network, the Received Signal Strength Indicator (RSSI), Packet Delivered Ratio (PDR) and Packet Sent Ratio (PSR) are used to check the high frequency interference in the studied signal. Simulations on real physiological datasets prove that the suggested solution achieves high detection accuracy (96.41%) while having a low false alarm rate (3.6%).
ISSN:2474-0446
DOI:10.1109/SSD54932.2022.9955801