An Analysis on CVE Vulnerabilities of the Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid expansion in the use of Internet of Things (IoT)-based system across different industries has forced manufacturers to make their products available for early release without including necessary security features. The communication between IoT and other system typically includes users personal data. A cyberattack on IoT-based system could potentially lead to exposure of sensitive information and can put security of other connected systems at risk. Analysis of vulnerability types in IoT-based system will help in the mitigation of any security related challenges. This analysis can motivate in the inclusion of secure design strategy during the manufacturing phase of IoT devices. Also these analysis can promote the use of cryptography techniques and secure coding practices in the software development phase for any product integrated to a IoT-based system. The main aim of this paper is to analyze common vulnerability types for IoT reported in the Common Vulnerabilities and Exposures List (CVE List) from 2019 till 2023. We also analyzed risk severity values of vulnerabilities which can give additional information to organization for creating priority to address these vulnerabilities. Vulnerability types are created to group related vulnerability for the analysis purposes. In our analysis we found the main reason for vulnerability in IoT-based applications and devices are related to Software bugs. In comparison with other IoT categories namely Devices, Application and Network vulnerabilities reported for Operating Systems flaws are very high.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it