Network calculus based modeling of anomaly detection
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
Anomalous activities such as flash crowd/event and denial of service (DoS) overload a pool of servers that hosts web contents. This is a great challenge for the 24 by 7 provision of Web contents and may result in interruption of the Web services. Therefore, it is very important that the occurrence of such activities is monitored so that in case of the occurrence of such anomalies, a remedial action can be in placed. In this paper, we introduce a novel technique to model the excessive legitimate or illegitimate requests (to a server farm/cluster) in terms of the concepts and terminology of Network Calculus. Network Calculus deals very well with flow rates (such as the rate of arrival and service patterns) and the capacity of a system. Simulation results show that the model successfully identifies anomalies such as DoS and flash events, and this indication can be used to start the remedial steps.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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