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 3.4<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i>, and detection time is around 1<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i>. 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 2<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i>, 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 (1<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i>+2<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i>=3<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i>) remains within the packet reporting time of 16.67<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i> for a 60 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hz</i> system.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".