{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iotdevid-a-behaviour-based-fingerprinting","title":"IoTDevID: A Behavior-Based Device Identification Method for the IoT","arxiv_id":"2102.08866","date":"2021-02-17","proceeding":"IEEE Internet of Things Journal 2022 12","authors":["Kahraman Kostas","Mike Just","Michael A. Lones"],"abstract":"Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine learning-based method, IoTDevID, that recognizes devices through characteristics of their network packets. As a result of using a rigorous feature analysis and selection process, our study offers a generalizable and realistic approach to modelling device behavior, achieving high predictive accuracy across two public datasets. The model's underlying feature set is shown to be more predictive than existing feature sets used for device identification, and is shown to generalize to data unseen during the feature selection process. Unlike most existing approaches to IoT device identification, IoTDevID is able to detect devices using non-IP and low-energy protocols.","url_abs":"https://arxiv.org/abs/2102.08866v3","url_pdf":"https://arxiv.org/pdf/2102.08866v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iotdevid-a-behaviour-based-fingerprinting","repo_url":"https://github.com/kahramankostas/IoTDevID","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"iotdevid-a-behaviour-based-fingerprinting","repo_url":"https://github.com/kahramankostas/iotdevidv2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"iot-device-identification","task_name":"IoT Device Identification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}