Towards insider threats detection in smart grid communication systems
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
In today's communication systems, the most damaging security threats are not originating from the outsiders but from the trusted insiders – both malicious insiders and negligent insiders. Always endowed with high privileges, insiders are significantly prone to conduct acts that can cause catastrophic damages to the whole system either intentionally or unintentionally. Characterised by the full and rapid integration of information and communication technologies, smart grid – arguably the largest national critical engineering infrastructure – is suffering from a multitude of security threats initiated from both outsiders and insiders. Without security guarantee, the promising benefits of achieving an efficient, green, and reliable power grid would not be a success. In this study, the authors investigate the insider threats and summarise the existing threats detection solutions in smart grid communication systems. In addition, a novel hybrid insider threats modelling, analysis, and detection framework, which is based on stochastic Petri net and behaviour rule specifications, is proposed to contain insider threats in smart grid communication systems.
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 | 0.000 |
| 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