Random Forest-Based DDOS Detection from Cpanel Logs with Real-Time Notification Integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study focuses on designing an automated program to detect Distributed Denial of Service (DDoS) attacks by analyzing access log data from CPanel. Using the Random Forest algorithm, the system processes large volumes of server log entries to distinguish between normal and malicious requests. Data preprocessing and model training are applied to optimize detection accuracy. To accelerate incident response, the detection module is integrated with Firebase Cloud Messaging (FCM), which delivers instant alerts to administrators when suspicious activity is identified. Experimental evaluation shows that the system achieves more than 95% accuracy on the test dataset, confirming its capability to reliably identify DDoS patterns. In comparison to manual analysis, the automated approach demonstrates superior speed, consistency, and operational efficiency, significantly reducing the time needed to recognize and respond to threats. The results indicate that combining machine learning-based detection with real-time notification is a practical and effective strategy for strengthening server security.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it