A Small World–Privacy Preserving IoT Device-Type Fingerprinting with Small Datasets

Internet-of-Things (IoT) device-type fingerprinting is the process of identification of the specific type of an IoT device based on its characteristics, such as network behavior. Such fingerprinting can be used to detect anomalous behavior of the device, or even predict its behavior should it get compromised. The typical approach to fingerprint an IoT device-type is by collecting a significant number of short network trace samples from these devices when it performs various activities and use machine learning on these samples to construct the fingerprint. There are several challenges to this approach. The first challenge is identifying the exact set of packets that correspond to the observed device-type behavior when it is performing some activity. The second challenge is that a single organization may not have enough data corresponding to all possible activities of the IoT device. We propose techniques to overcome the above mentioned challenges. First, to enhance device-type fingerprinting from small data sets, we designed a sliding-window based packet analysis behavioral model that provides improved data coverage associated with the activities of the tasks. Second, to get a model of the network behavior for the different activities of IoT devices deployed at various organizations, we use distributed deep-learning model so as to protect the privacy and confidentiality of the data. Finally, we alleviate the issue of data shortage by supplementing the training data with synthetic data generated using an Adversarial Autoencoder (AAE) neural network. We evaluated our approach using three different sets of experiments using a small set of representative devices. We estimate the best sliding window size for modeling device behavior by comparing the distributed learning performance over a range of window sizes. For our distributed approach, we achieve fingerprinting accuracy in the range of 94–99%, which is an improvement over the centralized approach for the same data sets and experiments. We demonstrate accuracy of \(97\%\), on-par with state-of-the-art fingerprinting approaches, when using synthetic training data generated by our AAE. We note that, this is the first such method of fingerprinting device-types in a collaborative privacy preserving manner while alleviating small data sets.