IoT Device Fingerprinting via Frequency Domain Analysis

The rapid proliferation of heterogeneous Internet of Things (IoT) devices has introduced a wide range of operational and security challenges, particularly in the domains of device identification and behavior profiling. Traditional fingerprinting methods, which rely primarily on time domain features, often fail to capture the complex, periodic, and often bursty nature of IoT communication—especially in environments characterized by sparse, irregular, or noisy traffic patterns. To address these limitations, two novel frequency-based fingerprinting techniques have been proposed: Spectral-Only Frequency Fingerprint (SFF) and Spectro-Correlative Frequency Fingerprint (SCFF). These approaches shift the analysis from the time domain to the frequency domain, enabling the extraction of richer and more robust behavioral signatures from network traffic. While SFF focuses on capturing the core spectral features of device traffic, SCFF extends this by incorporating inter-feature correlations, offering a more nuanced and comprehensive representation of device behavior. The effectiveness of SFF and SCFF is evaluated across multiple publicly available IoT datasets using a range of machine learning classifiers. Experimental results demonstrate that both fingerprinting methods significantly outperform traditional time domain approaches in terms of accuracy, precision, recall, and F1-score—across all tested classifiers and datasets.