SCI-IoT: A Quantitative Framework for Trust Scoring and Certification of IoT Devices
Shreyansh Swami, Ishwardeep Singh, Chinmay Prawah Pant

TL;DR
SCI-IoT introduces a standardized, quantitative framework for trust scoring and certification of IoT devices, assessing security and reliability across multiple domains with a transparent grading system.
Contribution
The paper presents a novel, unified trust evaluation framework with a six-tier grading model and weighted trust tests, enabling scalable certification of IoT devices.
Findings
Framework employs 30 trust tests across key security dimensions.
Devices are scored and certified using a weighted, normalized trust index.
Framework enhances transparency and scalability in IoT device certification.
Abstract
The exponential growth of the Internet of Things (IoT) ecosystem has amplified concerns regarding device reliability, interoperability, and security assurance. Despite the proliferation of IoT security guidelines, a unified and quantitative approach to measuring trust remains absent. This paper introduces SCI-IoT (Secure Certification Index for IoT), a standardized and quantitative framework for trust scoring, evaluation, and certification of IoT devices. The framework employs a six-tier grading model (Grades A-F), enabling device profiling across consumer, industrial, and critical infrastructure domains. Within this model, 30 distinct Trust Tests assess devices across dimensions such as authentication, encryption, data integrity, resilience, and firmware security. Each test is assigned a criticality-based weight (1.0-2.0) and a performance rating (1-4), converted to a normalized…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSecurity and Verification in Computing · IoT and Edge/Fog Computing · Advanced Malware Detection Techniques
