An IoT featureless vulnerability detection and mitigation platform

Bin Hulayyil, Sarah and Li, Shancang 2025. An IoT featureless vulnerability detection and mitigation platform. Electronics 14 (7) , 1459. 10.3390/electronics14071459

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Abstract

With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) algorithms, which are deep learning and large language models, to uncover IoT security vulnerabilities based on network traffic data directly without manual feature selection. The platform correctly identifies vulnerable and secure IoT devices just by raw network traffic! Experimental results show that the proposed study detects vulnerability with great accuracy by using pre-trained deep learning and LLM models, which facilitates direct extraction of vulnerability features from the dataset and therefore helps speed up the identification process. In addition, the design of the platform ensures that the models are accessible and can be easily applied by users with a user-friendly interface. Furthermore, the models with small sizes, 277.5 MB and 334 MB for the deep learning model and the LLM model, respectively, illustrated the potential use of the detection tool in practical settings. The ability to defend large-scale, diversified IoT ecosystems efficiently and in a scalable way by installing thousands of software manifestations quickly while exposing new applications to growing cyber threats is made possible by this significant advancement in the field of IoT security.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	MDPI
Date of First Compliant Deposit:	14 April 2025
Date of Acceptance:	25 March 2025
Last Modified:	14 Apr 2025 11:08
URI:	https://orca.cardiff.ac.uk/id/eprint/177628

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