Traceability Measurement Analysis of Sustained Internet-Wide Scanners via Darknet
Bibliographic record
Abstract
Darknet observation networks have experienced a significant surge in sophisticated and diverse Internet-wide scanning activities and investigative scanner operations. In addition to malware scanning attacks, reconnaissance scans to routinely probe the Internet space are also increasing in size to the point where they interfere with darknet data analysis. Since scans for investigative purposes that are not directly related to threats cause false positives in analysis, we desire to segregate them and focus on more substantive attack activities. Recent studies have proposed methods for analyzing these investigative scanners by clustering those with similar characteristics and tracing clusters based on IP addresses. However, these studies have primarily concentrated on specific cases and have yet to examine the traceability of all Internet-wide scanners comprehensively. In this study, we implement a naive cluster tracing method similar to those in existing studies to evaluate traceability. Specifically, we cluster scanners daily based on three features: the number of packets, the number of destination ports, and the number of destination IP addresses, and we assess traceability using the change rate of IP addresses and the distance of centroids. Our results reveal that approximately 98.4% of the scanners are difficult to trace, highlighting the need for more advanced and complex tracing methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".