Detecting Intra-enterprise Scanning Worms based on Address Resolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Signature-based schemes for detecting Internet worms often fail on zero-day worms, and their ability to rapidly react to new threats is typically limited by the requirement of some form of human involvement to formulate updated attack signatures. We propose an anomaly-based detection technique detailing a method to detect propagation of scanning worms within individual network cells, thus protecting internal networks from infection by internal clients. Our software implementation indicates that this technique is both accurate and rapid enough to enable automatic containment and suppression of worm propagation within a network cell. Our approach relies on an aggregate anomaly score, derived from the correlation of address resolution protocol (ARP) activity from individual network attached devices. Our preliminary analysis and prototype indicate that this technique can be used to rapidly detect zero-day worms within a very small number of scans
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it