A New Accelerated Attentive Deep Learning-Based Approach to Early Detect Attacks in Cyber-Physical Microgrids
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
The integration of renewable energy resources (RERs) and electric vehicles (EVs) into microgrids enables the provision of ancillary services for frequency and voltage regulation, thus improving the stability and efficiency. However, such integration requires a set of communication networks to exchange information among the microgrid components, which makes the microgrid assets prone to cyber vulnerability threats. Unlike in previous work, in which existing approaches wait until the impacts appear on the system to be able to detect the attacks, this paper introduces a new approach that combines the opening image technique and attentive deep learning to early detect the cyberattacks applied to a cyberphysical microgrid embedded with RERs and EVs. Furthermore, this paper investigated the effect of smart meters' data granularity on the attack detection accuracy. The results have shown that the use of a high time resolution of 1-sec increases the detection accuracy reaching 99.91%. The training process of the proposed approach has been accelerated using Graphics Processing Unit (GPU), which demonstrated low computational time by significantly reducing both the training and testing time by 93% and 70% respectively.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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