ExtendedSketch: Fusing Network Traffic for Super Host Identification With a Memory Efficient Sketch
Bibliographic record
Abstract
Super host refers to the host that has a high cardinality or exhibits a big change in a network. Facing big-volume network traffic, sketches have been widely applied to identify super hosts in an efficient and accurate way. However, most sketches cannot flexibly balance memory usage and accuracy in host cardinality estimation. Setting an inappropriate counter size for a sketch could either lead to inaccurate host cardinality estimation or cause memory waste. In order to solve this issue, we propose a novel extensible and reversible sketch, named ExtendedSketch, to achieve accurate super host identification with high memory efficiency. The core idea of ExtendedSketch is to monitor low-cardinality hosts with small-sized counters while dynamically extending the size of counters when monitoring high-cardinality hosts by applying an adaptive extension strategy. Such the strategy can adaptively increase counter size according to network traffic status at runtime, which not only ensures the accuracy of high-cardinality host estimation but also avoids unnecessary memory consumption. We perform theoretical analysis and conduct a series of experimental evaluations on ExtendedSketch based on real world network traffic. Experimental results show that under same memory usage, compared to the state-of-the-art, ExtendedSketch achieves$1.4{ \sim }7.5$times smaller error rate in estimating host cardinality with$1.9{ \sim }26.7$times better accuracy on super host identification and$95 {\sim }2^{15}$times faster speed on abnormal address reconstruction. Its advance in accuracy and efficiency demonstrates the practical significance of ExtendedSketch for super host identification.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".