<i>Network Attack Surface</i>: Lifting the Concept of Attack Surface to the Network Level for Evaluating Networks’ Resilience Against Zero-Day Attacks
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
The concept of attack surface has seen many applications in various domains, e.g., software security, cloud security, mobile device security, Moving Target Defense (MTD), etc. However, in contrast to the original attack surface metric, which is formally and quantitatively defined for a software, most of the applications at higher abstraction levels, such as the network level, are limited to an intuitive and qualitative notion, losing the modeling power of the original concept. In this paper, we lift the attack surface concept to the network level as a formal security metric for evaluating the resilience of networks against zero day attacks. Specifically, we first develop novel models for aggregating the attack surface of different network resources. We then design heuristic algorithms to estimate the network attack surface while reducing the effort spent on calculating attack surface for individual resources. Finally, the proposed methods are evaluated through experiments.
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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 | 0.004 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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