SLIoTDI: Scalable and Lightweight IoT Device Identification With Session-Level Grayscale Fingerprinting and Adversarial Training

The rapid expansion of the Internet of Things (IoT) has revolutionized various domains but also introduced critical security challenges, such as device spoofing and unauthorized access. These vulnerabilities underscore the urgent need for effective device identification to safeguard IoT networks and services. Despite ongoing research efforts, existing methods often fall short in scalability and lightweight design, which limits their deployment in real-world IoT environments. To address these challenges, we propose SLIoTDI, a novel scalable and lightweight IoT device identification approach. SLIoTDI uses session-level grayscale image-based fingerprinting and incorporates adversarial training with data augmentation to develop a robust and scalable feature extractor. Once trained, the extractor can generate fingerprints for unseen devices without retraining, ensuring adaptability to evolving IoT settings. Comprehensive experiments conducted on four public datasets and a real-world deployment validate the effectiveness of SLIoTDI, achieving the identification accuracies up to 99.98% and 99.16%, respectively. SLIoTDI is open-sourced to promote transparency and enable further research, offering a practical solution to enhance IoT security and device management in real-world applications.