A Time Series Approach for Inferring Orchestrated Probing Campaigns by Analyzing Darknet Traffic
Bibliographic record
Abstract
This paper aims at inferring probing campaigns by investigating dark net traffic. The latter probing events refer to a new phenomenon of reconnaissance activities that are distinguished by their orchestration patterns. The objective is to provide a systematic methodology to infer, in a prompt manner, whether or not the perceived probing packets belong to an orchestrated campaign. Additionally, the methodology could be easily leveraged to generate network traffic signatures to facilitate capturing incoming packets as belonging to the same inferred campaign. Indeed, this would be utilized for early cyber attack warning and notification as well as for simplified analysis and tracking of such events. To realize such goals, the proposed approach models such challenging task as a problem of interpolating and predicting time series with missing values. By initially employing trigonometric interpolation and subsequently executing state space modeling in conjunction with a time-varying window algorithm, the proposed approach is able to pinpoint orchestrated probing campaigns by only monitoring few orchestrated flows. We empirically evaluate the effectiveness of the proposed model using 330 GB of real dark net data. By comparing the outcome with a previously validated work, the results indeed demonstrate the promptness and accuracy of the proposed approach.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".