Assessing the Impact of Emerging Technologies on Cybersecurity with a Special Emphasis on Artificial Intelligence, the Internet of Things, and Blockchain Innovations
Bibliographic record
Abstract
This article proposes a safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things. Use Threat Intelligence Integration (TII), Dynamic Risk Assessment (DRA), Blockchain Integrity Verification (BIV), AI Adversarial Robustness Assessment (AARA), and IoT Security Compliance Assessment (ISCA). Each program is part of a larger, more linked defense system for sophisticated cyberthreats. The program uses Threat Intelligence Integration. Combining historical data with realtime hazard sources predicts assaults and their outcomes. The TII enabled new algorithms like DRA. The algorithms discover assets, assess weaknesses, and prioritize threats. Blockchain Integrity Verification (BIV) checks for issues and performs complicated hash and weight computations to secure the blockchain. AI Adversarial Robustness Assessment (AARA) evaluates AI models in BIV tests and other adversarial tasks. The ISCA ensures IoT devices fulfill AARA safety and security criteria. Each algorithm prioritizes tracking, updating, and assessing to keep up with the ever-changing risk situation. The recommended solution is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".