ArticleSensors (Basel, Switzerland)2023
Code Integrity and Confidentiality: An Active Data Approach for Active and Healthy Ageing.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
Funding
Abstract
Internet of Things cybersecurity is gaining attention as the number of devices installed in IoT environments is exponentially increasing while the number of attacks successfully addressed to these devices are also proliferating. Security concerns have, however, been mainly addressed to service availability and information integrity and confidentiality. Code integrity, on the other hand, is not receiving proper attention, mainly because of the limited resources of these devices, thus preventing the implementation of advanced protection mechanisms. This situation calls for further research on how traditional mechanisms for code integrity can be adapted to IoT devices. This work presents a mechanism for code integrity in IoT devices based on a virtual-machine approach. A proof-of-concept virtual machine is presented, specially designed for providing code integrity during firmware updates. The proposed approach has been experimentally validated in terms of resource consumption among the most-widespread micro-controller units. The obtained results demonstrate the feasibility of this robust mechanism for code integrity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.