A Probabilistic-Based Approach for Detecting Simultaneous Load Redistribution Attacks Through Entropy Analysis and Deep Learning
Bibliographic record
Abstract
Load redistribution attacks (LRAs) are one of the most sneaky and realistic types of false data injection attacks (FDIAs), in which the attacker manipulates the measurements in a way that depicts a false image of the system situation for the operator. Due to the uncertainty in the system’s parameters, system modeling (AC or DC), and so on, detection LRAs have a lot of challenges. To overcome the difficulty of devising a general mechanism for LRA detection based on deterministic methods, we propose a probabilistic approach based on entropy analysis and deep learning. The ratio of cyber loads to real loads (RCLRLs) is the major input of the proposed detection algorithm to make the presented method applicable for different load levels in the system. By extracting the entropy of RCLRLs under LRAs, our method reduces dependency on system modeling and the system’s parameters. We employ the bias correction method on forecasted loads to approximate the real load in the system, enhancing our approach’s accuracy. The framework is a decentralized algorithm that detects simultaneous LRAs in different areas and ensures scalability for large systems. Simulations on the IEEE 118-bus systems demonstrate the proposed method’s high accuracy and rapid response.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".