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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".