IoT-SafeMod: Model-Driven Security and Safety for IoT Applications

The Internet of Things (IoT) is transforming everyday life by automating tasks through platforms such as SmartThings, OpenHab, IFTTT, and Alexa. However, early developments in IoT often encounter challenges related to verification, particularly for non-technical users and across various IoT platforms. To address this issue, we present, IoT-SafeMod, a Model-Driven Engineering (MDE) tool that utilizes UML models with integrated real-time verification. This tool helps designers address security and safety issues proactively during the mod-eling phase. This paper discusses the development and effectiveness of IoT-SafeMod in ensuring secure and safe IoT systems through early verification in the development process.

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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/serp4iot66600.2025.00013
Citations
1
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.49
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IoT-SafeMod: Model-Driven Security and Safety for IoT Applications

Manar H. Alalfi, Lobna Abuserrieh
1 citations
Advanced Malware Detection Techniques
1.49
article

IoT-SafeMod: Model-Driven Security and Safety for IoT Applications

Manar H. Alalfi, Lobna Abuserrieh
article en
1 citations

Abstract

The Internet of Things (IoT) is transforming everyday life by automating tasks through platforms such as SmartThings, OpenHab, IFTTT, and Alexa. However, early developments in IoT often encounter challenges related to verification, particularly for non-technical users and across various IoT platforms. To address this issue, we present, IoT-SafeMod, a Model-Driven Engineering (MDE) tool that utilizes UML models with integrated real-time verification. This tool helps designers address security and safety issues proactively during the mod-eling phase. This paper discusses the development and effectiveness of IoT-SafeMod in ensuring secure and safe IoT systems through early verification in the development process.

Toronto Metropolitan University (CA)
Openalex Percentile: Top 18%
Advanced Malware Detection Techniques
1.49
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