A-IDFE: An Adaptive and Robust Deep Fingerprinting Framework for IoT Device Identification

Accurate identification of IoT devices is essential for network management and security enforcement in increasingly heterogeneous environments. Existing fingerprinting approaches rely on static subspace modeling and shallow fusion strategies, which limit their adaptability and discriminative power. In this paper, we propose A-IDFE (Adaptive IoT Device Fingerprint Extraction), a novel deep learning framework that enhances fingerprint robustness, adaptability, and generalization for device identification. A-IDFE introduces (a) a unified encoder with adaptive channel attention to capture heterogeneous traffic subspaces efficiently, (b) a Transformer-based fusion module that models cross-subspace dependencies, (c) a discriminative embedding learning objective using ArcFace to improve inter-class separability, and (d) a robustness-aware training strategy incorporating perturbation and adversarial augmentations. Extensive experiments on public and private IoT datasets demonstrate that A-IDFE outperforms existing methods in both identification accuracy and robustness against noisy, adversarial, and cross domain conditions, making it a promising solution for practical IoT device fingerprinting.