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
Conventional power network capabilities have improved and enhanced by the smart grid but at the same time making it more vulnerable to different types of attacks. These vulnerabilities allow an attacker to breakdown integrity and confidentiality and permit access to the network. Intrusion Detection System (IDS) is one of the significant ways to provide secure and reliable services in a smart grid environment. In this paper, we propose intrusion detection framework for the smart grid. We consider the three-layer architecture of smart grid system. The proposed framework has an IDS in each HAN and NAN and many IDS sensors in WAN. Any malicious activity will be sent to the central management unit; the central management unit correlates and investigates alerts produced by various distributed sensors using anomaly based detection methodology. IDS management system will collect and preprocess the alerts of all sensors and correlate these alerts to distinguish symptoms of attack and contravention of security policy. The ISCX2012 dataset was used to analyze and select the most efficient classifier for anomaly based intrusion detection.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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