Utilization and Security Protection of Computer Communication Technology in the Information Age
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
With the advent of the information age, computer communication technology has been widely applied, promoting the development of various industries. However, the accompanying information security issues have attracted widespread attention. This article explored the current application status of computer communication technology in different fields, analyzed its potential threats in information security, and explored protective measures using CNN (Convolutional Neural Networks) models, aiming to provide guidance and suggestions for industry practitioners. The research structure showed that the average accuracy of the CNN model was 94.8%, significantly better than the 89.5% of the SVM (Support Vector Machine) model. The average response time of the CNN model was only 20.5 milliseconds. The average false alarm rate of the CNN model on the false alarm rate indicator was 7.2%. In the final system overhead experiment, the CNN model required a significant amount of system resources in high traffic environments. From the above data conclusions, it can be seen that the CNN model exhibited higher efficiency and accuracy in network communication security, despite the high resource demand under high load conditions.
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 | 0.001 | 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 | 0.008 |
| Open science | 0.000 | 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