Evolution and Trends in Artificial Intelligence of Things Security: When Good Enough is Not Good Enough!
Bibliographic record
Abstract
Artificial Intelligence of Things (AIoT) combines the power of artificial intelligence, computing power, and IoT infrastructure. With AIoT, artificial intelligence (AI) is embedded in computing devices, all connected to one or more IoT networks. The mutual benefit of these two technologies allows for a different vision and a broader scope of action. It will enable the analysis of Big Data, make decisions and act on data without human intervention. Although there have been gradual advancements in IoT Security, most connected machines and devices have been built with security in mind as a second thought, where the basic minimum standards are “good enough.” However, given the increasing importance of data security in today's world, providing additional layers of security at the network and device level is paramount, especially for critical applications such as government, defense, and healthcare. This paper will provide the current and future security techniques to countermeasure the cybersecurity threats facing the IoT and AIoT.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".