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oai:mountainscholar.org:10217/2426752026-01-21T15:24:07Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_182111col_10217_100389

Bar-on, Maxwel A., author Ray, Indrakshi, advisor Bezawada, Bruhadeshwar, committee member Ray, Indrajit, committee member Jayasumana, Anura, committee member 2026-01-12T11:27:40Z 2025 Baron_colostate_0053N_19301.pdf https://hdl.handle.net/10217/242675 https://doi.org/10.25675/3.025567 Zip file contains thesis presentation. The rapid growth of the Internet-of-Things (IoT) industry has introduced new attack surfaces in home and enterprise networks due to insufficient built-in security measures in many IoT devices. IoT network fingerprinting, the process of identifying IoT devices from their network communication patterns, can be used to select appropriate access controls for vulnerable IoT devices, offering a promising solution to this security problem. Typical state-of-the-art IoT fingerprinting approaches identify devices by applying deep learning models to samples of their network traffic. This work addresses 5 challenges of using deep learning for IoT fingerprinting: (1) traffic collected by a single observer may be insufficient for training an accurate fingerprinting model; (2) variations in communication rates among different IoT devices results in imbalanced datasets, which can lead to biased models; (3) it is difficult for a model to capture the relationships between packets when some packets belong to separate flows; (4) it is inefficient to adapt an existing IoT fingerprinting model to identify new devices; and (5) relying on fixed-length samples of traffic can result in arbitrarily long fingerprinting times. For the first challenge, we propose a federated learning approach for training a fingerprinting model using traffic collected by multiple separate observers. For the second challenge, we propose a hierarchical Mixture-of-Experts that balances training data by grouping devices based on their communication rates before identifying them. For the third challenge, we propose a relative encoding technique for packet endpoints that preserves relationships across flows. For the fourth challenge, we propose a bi-component fingerprinting architecture that efficiently adapts to new devices by reusing a portion of its parameters. Finally, for the fifth challenge, we propose a fixed-time traffic sampling approach. We evaluate our proposed approaches through a series of experiments and demonstrate how each one overcomes its associated challenge in an experimental setting. born digital masters theses ZIP PDF English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. federated learning Internet of Things transfer learning fingerprinting deep learning network security Deep learning for IoT fingerprinting Text Image Master of Science (M.S.) Masters Computer Science Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access