Knowledge Extraction and Integration for Information Gathering in 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
Assets identification is an important aspect of penetration test on which security practitioner develop their defense mechanism. In addition, assets identification is an essential piece of information for penetration testers to find a weakness in the targeted organization. Information gathering is the process of extracting knowledge to recognize the organizations' assets available on the internet. There are many open source tools available for information gathering. However, penetration tester needs to put manual effort (during several hours to multiple days) to extract useful knowledge from the output of one tool and integrate that knowledge in another tool. Penetration tester can increase speed and accuracy of the overall information gathering process by automating the knowledge extraction and integration. This paper review and identify open source subdomain enumeration and service scanning tools and present an approach to integrate and automate identified tools. The result reveals that there is a significant improvement of the information gathering process by using our approach due to the reduction of manual tasks.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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