Wireless Signal Fingerprinting Framework Based on Emphasized Spectral Features for IoT Device Authentication

Bluetooth Low Energy (BLE) is widely used in Internet of Things (IoT) devices due to its low power consumption and efficient wireless communication. However, BLE-based systems remain vulnerable to signal-level attacks, such as spoofing and signal forgery, which allow adversaries to impersonate legitimate devices and compromise system security. Existing security approaches mainly rely on cryptographic mechanisms or protocol-level features, while conventional signal fingerprinting methods often fail to capture subtle device-specific variations across the frequency spectrum. We propose a deep-learning-based BLE signal fingerprinting framework that uses emphasized spectral data to enhance device authentication. The proposed framework selectively highlights frequency regions exhibiting pronounced hardware-dependent variations using a hybrid filter bank design and extracts spectral features for anomaly-based device identification. Experimental evaluations conducted on BLE signals collected from multiple devices demonstrate that the proposed approach outperforms conventional methods, achieving superior authentication performance. By leveraging emphasized frequency-domain characteristics, we provide an effective authentication method for BLE-based IoT environments.