A Survey on Verification of Security and Safety in IoT Systems
Bibliographic record
Abstract
Internet of Things (IoT) has been rapidly growing in the past few years in all life disciplines. IoT provides automation and smart control across various domains, including home automation, healthcare, and automotive. Given the tremendous number of connected IoT devices, this growth leads to enormous automatic interactions among sizable IoT apps in their environment, making IoT apps smarter and more interesting to their users. However, unintended interactions and potential malicious behaviors within IoT apps can pose serious security and safety risks, particularly for non-expert users unfamiliar with their IoT automation processes. Therefore, robust verification tools are crucial to ensure these systems are safe and secure. In this light, this paper surveys current tools and approaches designed to verify security and safety properties in IoT systems. Our survey explores program analysis techniques utilized in the current literature to verify IoT applications’ security and safety. Furthermore, our paper introduces classification and categorization attributes that help understand the research landscape within this domain. We conclude by discussing challenges with current verification techniques and propose potential solutions to support the verification of IoT systems’ security and safety. The results from our survey are significant, as they can guide future research efforts in developing IoT systems that are more secure and safer for all users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".