A GAN-Based False Data Injection and Civil Attack Detection Framework for Digital Relays with Feature Selection
Bibliographic record
Abstract
As one of the highly used components in the power system, digital relays are employed to use the phasor measurement units', voltage, and current signals, to decide whether a fault has occurred in the system or not. The major difference between digital relays and traditional ones is that the former receives the mentioned signals mostly by wireless communication paths, instead of using measurement transformers. While the wireless signals used as the input of digital relays are vulnerable to cyber-attacks, a proper cybersecurity solution for digital relays is demanding. Due to the pervasive use of AI-based techniques in cybersecurity, one of the most vital features of a cybersecurity method is its training data volume. Hence, we employed a modified generative adversarial network that is able to generate attacked data by itself, meaning that there is no need to train the neural network with the cyber-attacked data. Additionally, a feature selection method called extra tree classifier is used to reduce the dimension of our input data. The discriminator sector of the generative adversarial network is then used as the classifier to detect false data injection, civil attacks, and faults which is preventing the digital relay from tripping falsely. The two mentioned cyber-attacks, false data injection and civil-attack, are used to evaluate the performance of our proposed method in 29 different scenarios. The IEEE 39-bus transmission network is employed as our test system. The proposed cyber-attack detection method was able to classify the faults and mentioned cyber-attacks in most scenarios with more than 97 percent of accuracy, f1 score, and more than 96 percent of sensitivity. We also examined the proposed modified GAN-based method by giving it only the voltage signal as input for training, and the method scored at least 90 percent of accuracy, 87 percent of sensitivity, and 88 percent of f1 score in harsh scenarios which is almost as accurately as before when both voltage and current signals were used for the relay as inputs, making the proposed technique universal and suitable for other types of relays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".