Detecting and Localizing Wireless Spoofing Attacks on the Internet of Medical Things

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively.