Hardening of network segmentation using automated referential penetration testing
Bibliographic record
Abstract
We study the problem of hardening the security of existing networks. Dynamic and static analysis are two main approaches that are used to address this problem. Dynamic analysis is performed using penetration testing. Penetration testing (short pentesting) simulates attacks on an existing and possibly dynamic network to identify its vulnerabilities without causing it any harm. Static analysis analyzes the access control policies of both network resources and firewalls without executing them. Although, dynamic and static analysis are extremely useful approaches, they also have several drawbacks. For instance, they do not identify vulnerabilities resulting from weak network segmentation, improper implementation of the Defence in Depth strategy, and an increased attack surface for a given resource or firewall. In this paper, we propose a novel approach termed as the referential penetration testing (RPT) approach to evaluate the security of networks. The RPT approach evaluates the network and checks for segmentation flaws, while checking for other traditional vulnerabilities. We then propose a novel framework, called the RPT framework, that incorporates the RPT approach to identify vulnerabilities in networks. Finally, we implement this framework in the Software Defined Network (SDN) environment and discuss its usefulness.
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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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".