Bar-on, Maxwel, authorPatterson, Katherine, authorBezawada, Bruhadeshwar, authorRay, Indrakshi, authorRay, Indrajit, authorACM, publisher2025-09-252025-09-252025-07-25Maxwel Bar-on, Katherine Patterson, Bruhadeshwar Bezawada, Indrakshi Ray, and Indrajit Ray. 2025. "Bring your own device!": Adaptive IoT Device type Fingerprinting using Automatic Behavior Extraction: [Work In Progress Paper]. In Proceedings of the 30th ACM Symposium on Access Control Models and Technologies (SACMAT '25), July 8-10, 2025, Stony Brook, NY, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3734436.3734456https://hdl.handle.net/10217/242037Internet-of-Things (IoT) is playing a key role in modern society by offering enhanced functionalities and services. As IoT devices may introduce new security risks to the network, network administrators profile the behavior of IoT devices using device fingerprinting. Device fingerprinting typically involves training a machine learning model using the network behavioral data of existing devices. If a new device is added, the network becomes vulnerable to attacks until the time that the machine learning model is trained and updated to integrate the new device. Furthermore, if many devices are regularly added to the network, the cost of adapting the machine learning model can be significant. To address the challenges of security and scalability in fingerprinting, we create a collection of observed behaviors of IoT devices from existing devices and use this collection to construct a fingerprint for a new device. In our approach, we design a bi-component neural network architecture consisting of a transformer-based behavior-extractor (BE) and a fingerprinting interpreter. We perform a one-time training of the BE to extract behaviors from known devices. We use the generated BE for (a) fingerprinting existing devices and (b) adapting the existing fingerprinting model to new device data. In our experiments on 22 diverse IoT devices, we show that our model can identify newly introduced devices as well as known devices with a high identification rate. Our approach improves the time to adapt a model by a factor of 78.3× with no loss of accuracy, achieving recall over 98%.born digitalarticleseng©Maxwel Bar-on, et al. ACM 2025. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SACMAT '25, https://dx.doi.org/10.1145/3734436.3734456.IoTfingerprintingtransformerself-supervised learningTexthttps://doi.org/10.1145/3734436.3734456