Analysis of Security Vulnerabilities and Threats of Intelligent Devices in the Internet of Things and Countermeasures
Bibliographic record
Abstract
This paper aims to deeply analyze the security vulnerabilities and threats of IoT smart devices, and put forward effective countermeasures. Through systematic research and analysis, this paper first summarizes the development and popularization of IoT smart devices, and emphasizes the importance of IoT security in today's society. Then, the common types of security vulnerabilities in IoT smart devices and their causes are analyzed in detail, as well as the impact of these vulnerabilities on IoT systems. At the same time, the article also reveals the serious consequences of major IoT security vulnerabilities. After deeply discussing the threats faced by IoT smart devices, this paper puts forward a series of specific strategies and suggestions, including strengthening identity authentication and access control, regularly updating and repairing security vulnerabilities, strengthening data encryption and communication security, establishing a sound security audit and monitoring mechanism, enhancing users' security awareness and education, and calling for the support and improvement of policies and regulations. In order to provide valuable reference and suggestions for manufacturers, users and policy makers of IoT equipment, and jointly promote the safe development of IoT industry.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".