Lightweight Threshold-Based Real-Time Distributed Denial of Service Attack Detection with WebSocket Alerts
Bibliographic record
Abstract
As digital platforms increasingly dominate day-to-day activities, ensuring robust and reliable security mechanisms has become a critical necessity.Among various cyber threats, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are widely studied, as they directly threaten service availability by overwhelming systems with excessive requests, making services inaccessible to legitimate users.Conventional DDoS detection techniques are often unsuitable for lightweight deployments because they rely heavily on expensive hardware or complex machine learning (ML) models.This work proposes a lightweight and scalable DDoS anomaly detection framework capable of identifying real-time anomalies from server log data.The system visualizes abnormal traffic patterns using WebSocket communication with a Node.jsserver.The web application, hosted on Google Cloud Storage (GCS), includes a real-time monitoring dashboard that is updated regularly.Experimental findings confirm the effectiveness of the proposed system in identifying high request rates with low latency, achieving 0.002 s latency and approximately 97.0% alert accuracy.The proposed solution is particularly suitable for small-to medium-sized online platforms, as it provides a cost-effective, scalable, and efficient real-time DDoS detection approach without requiring complex and intensive resource infrastructure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".