Application of Artificial Intelligence Technology in Network Security Protection of Power Enterprise
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
This article aims to explore how artificial intelligence (AI) technology empowers power enterprises to build a new generation of proactive, intelligent, and adaptive cybersecurity protection systems. The article first analyzes the new types of cybersecurity threats faced by power systems and their severe challenges to critical information infrastructure, and expounds on the limitations of traditional security protection methods. Furthermore, it systematically discusses the specific application paradigms of AI core technologies such as machine learning, deep learning, and natural language processing in enterprise network boundaries, production control areas (II/III zones), and wide-area environments, including intelligent threat detection, abnormal behavior analysis, security situation awareness and prediction, and automated response. Finally, this article prudently assesses the challenges faced by AI applications, such as data quality, model interpretability, adversarial attacks, and compliance, and looks forward to the future development direction of technology and business integration.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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