IoT Device Identification Using Device Fingerprint and Deep Learning

Abstract

The foundation of security in IoT devices lies in their identity. However, traditional identification parameters, such as MAC address, IP address, and IMEI, are vulnerable to sniffing and spoofing attacks. To address this issue, this paper proposes a novel approach using device fingerprinting and deep learning for device identification. Device fingerprinting is generated by analyzing inter-arrival time (IAT), round trip time (RTT), or IAT/RTT outliers of packets used for communication in networks. We trained deep learning models, namely convolutional neural network (CNN) and CNN + LSTM (long short-term memory), using device fingerprints generated from TCP, UDP, ICMP packet types, ICMP packet type, and their outliers. Our results show that the CNN model performs better than the CNN + LSTM model. Specifically, the CNN model achieves an accuracy of 0.97 using the IAT device fingerprint of ICMP packet type, and 0.9648 using the IAT outlier device fingerprint of ICMP packet type on a publicly available dataset from the crawdad repository.