Enhancing Private Cloud Security Using Knowledge Understanding Assessment Defense Method for Distributed Denial of Service Attack Mitigation
Bibliographic record
Abstract
Cloud computing provides significant flexibility and scalability; however, it is still susceptible to Distributed Denial of Service (DDoS) attacks, which pose a risk to service availability.This research presents an improved mitigation framework that incorporates the Knowledge Understanding Assessment Defense (KUAD) method within a private cloud environment utilizing OwnCloud.Simulations of Goldeneye-based DDoS attacks were conducted, with network performance being monitored through the use of Snort, Wireshark, nload, and iPerf.The attack resulted in a significant rise in network load, elevating jitter from an average of 0.1561 ms to 0.1519 ms and amplifying packet loss from 0.24% to 0.89%.The mitigation phase, which involved blocking attacker IP addresses, effectively restored service stability, minimized jitter, and greatly decreased packet loss.The results indicate that the KUAD framework facilitates the acquisition of forensic evidence while also allowing for prompt recovery through its built-in mitigation mechanism.The research presents a practical and adaptive defense model aimed at strengthening private cloud resilience in the face of DDoS attacks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".