Hardening of network segmentation using automated referential penetration testing
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
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. The proposed framework is an application of the Digital Twin Technology in network security. We compare RPT to the network vulnerabilities assessment tools Nessus, OpenVAS, Qualys, Illumio, Tufin, and AlgoSec. The comparison reveals that RPT is different from the other tools on all the considered technical aspects, which indicates that it brings a novel approach to assess network segmentation. It has a very limited focus compared to the others, which makes it suitable for being used in combination with anyone of them to further enhance the robustness of the segmentation. Finally, we implement this framework in the Software Defined Network (SDN) environment and discuss its usefulness.
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 | 0.001 | 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