AI-BASED RF FINGERPRINTING DEVICE IDENTIFICATION AND WIRELESS SECURITY

Abstract

AI-powered RF fingerprinting contributes to the security of emerging networks, authenticating received signal characteristics through an artificial intelligence mechanism. This method is rooted in an AI-based approach. The RF fingerprinting technique is adept at identifying distinctive RF features, which is crucial for optimizing security in wireless networks. Our investigation delves into AI-based RF fingerprinting, employing recurrent neural networks coupled with long short-term memory to classify and analyze signals. We address challenges arising from signal variability, environmental factors, and device introductions. The primary focus is on the efficacy of RF fingerprints for authentication across diverse communication technologies. The methodology encompasses data collection training, showcasing a high accuracy rate in transmitter classification. This study emphasizes the considerable potential of AI-based RF fingerprinting in mitigating security and identification challenges in the era of the Internet of Things (IoT).