On detecting and clustering distributed cyber scanning
Bibliographic record
Abstract
This paper proposes an approach that is composed of two techniques that respectively tackle the issues of detecting corporate cyber scanning and clustering distributed reconnaissance activity. The first employed technique is based on a non-attribution anomaly detection approach that focuses on what is being scanned rather than who is performing the scanning. The second technique adopts a statistical time series approach that is rendered by observing the correlation status of a traffic signal to perform the identification and clustering. To empirically validate both techniques, we experiment with two real network traffic datasets and implement two proof-of-concept environments. The first dataset comprises of unsolicited one-way telescope/darknet traffic while the second dataset has been captured in our lab through a customized setup. The results show, on one hand, that for a class C network with 250 active hosts and 5 monitored servers, the proposed detection technique's training period required a stabilization time of less than 1 second and a state memory of 80 bytes. Moreover, in comparison with Snort's sfPortscan technique, it was able to detect 4215 unique scans and yielded zero false negative. On the other hand, the proposed clustering technique is able to correctly identify and cluster the scanning machines with high accuracy even in the presence of legitimate traffic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".