A Supervised Machine Learning and Temporal Convolutional Network Based Framework for Denial-of-Service Attack Detection and Mitigation
Bibliographic record
Abstract
This article proposes a supervised machine learning (ML) based strategy for detecting Denial-of-Service (DoS) attacks and a Temporal Convolutional Network (TCN) based mitigation approach for synchrophasor data transmitted from Phasor Measurement Units (PMUs) to a Phasor Data Concentrator (PDC). The proposed work addresses the urgent need for real-time detection and reconstruction of DoS-affected data loss in synchrophasor networks, which is essential for secure and reliable grid operation. It enables deployment-ready detection with low computational overhead. After decoding and storing synchrophasor data for training, feature extraction is performed, followed by dimensionality reduction using Principal Component Analysis (PCA). Subsequently, the dataset is classified using various ML models with their hyperparameters selected via Randomized Search Cross-Validation (RSCV). A meta-learning layer is introduced that adapts to any dataset based on its statistical features and selects the best-performing model, ensuring accurate DoS attack detection and minimal detection time. The total meta-training time is approximately 3.4s, and detection time is around 1ms. Further, a Euclidean distance-based trust score is used to trigger retraining in the presence of concept drift. Once a DoS attack is detected, TCN reconstructs the lost data using historical data within approximately 2ms, mitigating adverse effects. The methodology is validated using a cyber-physical testbed simulating synchrophasor data flow from multiple PMUs to a PDC under various DoS scenarios. The outcomes indicate that the meta-learning layer selects the optimal model, and TCN effectively reconstructs missing phasors. The overall processing time (1ms+2ms=3ms) remains within the packet reporting time of 16.67msfor a 60Hzsystem.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".