PolicyVis: firewall security policy visualization and inspection
Bibliographic record
Abstract
Firewalls have an important role in network security. However, managing firewall policies is an extremely complex task because the large number of interacting rules in single or distributed firewalls significantly increases the possibility of policy misconfiguration and network vulnerabilities. Moreover, due to low-level representation of firewall rules, the semantic of firewall policies become very incomprehensible, which makes inspecting of firewall policy's properties a difficult and error-prone task. In this paper, we propose a tool called PolicyVis which visualizes firewall rules and policies in such a way that efficiently enhances the understanding and inspecting firewall policies. Unlike previous works that attempt to validate or inspect firewall rules based on specific queries or errors, our approach is to visualize firewall policies to enable the user to place general inquiry such as does my policy what I intend to do unrestrictedly. We describe the design principals in PolicyVis and provide concepts and examples dealing with firewall policy's properties, rule anomalies and distributed firewalls. As a result, PolicyVis considerably simplifies the management of firewall policies and hence effectively improves the network security.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".