Version 1
: Received: 13 October 2024 / Approved: 14 October 2024 / Online: 14 October 2024 (14:26:53 CEST)
How to cite:
Rizvi, S.; Govere, E. A Risk Management Framework for IoT Devices and Networks. Preprints2024, 2024101083. https://doi.org/10.20944/preprints202410.1083.v1
Rizvi, S.; Govere, E. A Risk Management Framework for IoT Devices and Networks. Preprints 2024, 2024101083. https://doi.org/10.20944/preprints202410.1083.v1
Rizvi, S.; Govere, E. A Risk Management Framework for IoT Devices and Networks. Preprints2024, 2024101083. https://doi.org/10.20944/preprints202410.1083.v1
APA Style
Rizvi, S., & Govere, E. (2024). A Risk Management Framework for IoT Devices and Networks. Preprints. https://doi.org/10.20944/preprints202410.1083.v1
Chicago/Turabian Style
Rizvi, S. and Ephraim Govere. 2024 "A Risk Management Framework for IoT Devices and Networks" Preprints. https://doi.org/10.20944/preprints202410.1083.v1
Abstract
The Internet of Things (IoT), as a technology, transforms every day, consumer devices into devices capable of collecting and transmitting data. This momentous progress highlights the need for reliable risk management frameworks that address the potential risks associated with IoT devices across all aspects of life. Traditionally, the feasibility of IoT was limited by the high costs of sensors and their connectivity. Although, recent advancements have greatly reduced costs, enabling widespread connectivity of devices to the Internet. Consequently, numerous IoT devices and networks are left vulnerable without a comprehensive framework for managing these risks effectively. This paper introduces a more systematic framework designed to counter security risks and safeguard IoT devices. This framework takes a step-by-step approach for focusing on mitigating risks arising from inadequate security designs for IoT devices. It involves procedures for risk identification, evaluation, and prioritization which is followed by comprehensive risk analysis and control measures, and because risk evaluation is tedious, we suggest using machine learning (ML) to accelerate the risk evaluation process, boost the precision and consistency of risk assessments, and provide valuable insight, which ultimately enables risk analysts to make better informed and timely decisions. Through multiple case studies, we demonstrate the practicality and efficacy of the framework for evaluating IoT device risks and guiding the implementation of appropriate controls to safeguard devices and networks. We also develop security dashboards that provide visual summaries comparing device values, threat assessments, and risk mitigation costs, aiding in clear communication and prioritization of security measures.
Keywords
risk assessment; IoT security; control strategies; vulnerabilities management; security threats and attacks; device‐level security
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
