Accessible from the open web: a qualitative analysis of the available open-source information involving cyber security and critical infrastructure
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
Abstract In order to efficiently manage and operate industrial-level production, an increasing number of industrial devices and critical infrastructure (CI) are now connected to the internet, exposed to malicious hackers and cyberterrorists who aim to cause significant damage to institutions and countries. Throughout the various stages of a cyber-attack, Open-source Intelligence (OSINT) tools could gather data from various publicly available platforms, and thus help hackers identify vulnerabilities and develop malware and attack strategies against targeted CI sectors. The purpose of the current study is to explore and identify the types of OSINT data that are useful for malicious individuals intending to conduct cyber-attacks against the CI industry. Applying and searching keyword queries in four open-source surface web platforms (Google, YouTube, Reddit, and Shodan), search results published between 2015 and 2020 were reviewed and qualitatively analyzed to categorize CI information that could be useful to hackers. Over 4000 results were analyzed from the open-source websites, 250 of which were found to provide information related to hacking and/or cybersecurity of CI facilities to malicious actors. Using thematic content analysis, we identified three major types of data malicious attackers could retrieve using OSINT tools: indirect reconnaissance data, proof-of-concept codes, and educational materials. The thematic results from this study reveal an increasing amount of open-source information useful for malicious attackers against industrial devices, as well as the need for programs, training, and policies required to protect and secure industrial systems and CI.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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