ProvTalk: Towards Interpretable Multi-level Provenance Analysis in Networking Functions Virtualization (NFV)
Bibliographic record
Abstract
Network functions virtualization (NFV) enables agile deployment of network services on top of clouds.However, as NFV involves multiple levels of abstraction representing the same components, pinpointing the root cause of security incidents can become challenging.For instance, a security incident may be detected at a different level from where its root cause operations were conducted with no obvious link between the two.Moreover, existing provenance analysis techniques may produce results that are impractically large for human analysts to interpret due to the inherent complexity of NFV.In this paper, we propose ProvTalk, a provenance analysis system that handles the unique multi-level nature of NFV and assists the analyst to identify the root cause of security incidents.Specifically, we first define a multi-level provenance model to capture the dependencies between NFV levels.Next, we improve the interpretability through three novel techniques, i.e., multi-level pruning, mining-based aggregation, and rule-based natural language translation.We implement ProvTalk on a Tacker-OpenStack NFV platform and validate its effectiveness based on real-world security incidents.We demonstrate that ProvTalk captures management API calls issued to all NFV services, and produces more interpretable results by significantly reducing the size of the provenance graphs (about 3.6 times reduction via the multi-level pruning scheme and two times reduction via the aggregation scheme).Our user studies show that ProvTalk facilitates the analysis task of real-world users by generating more interpretable results.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".