Digital Evidence Security System Design Using Blockchain Technology
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
Digital evidence plays an essential role in meeting the forensic need to uncover cybercrime and search for trace information of perpetrators.Digital evidence is vulnerable to system changes, human error, theft, deletion, and data manipulation, requiring security efforts to maintain authenticity.This study offers optimization of the chain of custody systems to maintain digital evidence integrity using authentication applications connected to the website server database.The design of the chain of custody system uses blockchain technology and K-means clustering algorithm.This research process consists of two stages.The first stage is the prototype of blockchain-based user access authentication applications.The second stage is the implementation of K-means clustering to determine the place of data storage according to its classification.The results of this study are the maximum security for blockchain-based chain of custody with the efficiency value of this application of 94.73% and the system load value of 0.223%.The total cost of deploying the application is 0.026702786 ETH.Based on this research can help to secure digital evidence information.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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