Revisiting network scanning detection using sequential hypothesis testing
Bibliographic record
Abstract
ABSTRACT Network scanning is a common, effective technique to search for vulnerable Internet hosts and to explore the topology and trust relationships between hosts in a target network. Given that the purpose of scanning is to search for responsive hosts and network services, behavior‐based scanning detection techniques based on the state of inbound connection attempts remain effective against evasion. Many of today's network environments, however, feature a dynamic and transient nature with several network hosts and services added or stopped (either permanently or temporarily) over time. In this paper, working with recent network traces from two different environments, we re‐examine the Threshold Random Walk (TRW) scan detection algorithm, and we show that the number of false positives is proportional to the transiency of the offered services. To address the limitations found, we present a modified algorithm (Stateful Threshold Random Walk (STRW) algorithm) that utilizes active mapping of network services to take into account benign causes of failed connection attempts. The STRW algorithm eliminates a significant portion of TRW false positives (e.g., 29% and 77% in two datasets studied). Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".