Editorial for the Special Issue on Sustainable Cyber Forensics and Threat Intelligence
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
The papers in this special issue focus on sustainable cyber forensics and threat intelligence. Increasing societal reliance on interconnected digital systems, including smart grids and Internet of Things (IoT), made sustainable detection and investigation of threat actors among the highest priorities of any society. Scale and attack surface of modern networks mandate optimized deployment of limited cyber forensics and threat intelligence resources to detect and remove malicious actors in a timely manner. However, timely dealing with such a huge number of attacks is not possible without employment of artificial intelligence and machine learning techniques. When a significant amount of data is collected from or generated by different security monitoring solutions, intelligent big-data analytical techniques are necessary to mine, interpret and extract knowledge out of those data. The emerging field of cyber threat intelligence is investigating applications of artificial intelligence and machine learning techniques to perceive, reason, learn and act intelligently against advanced cyber attacks. A crucial success factor in implementation, installation and deployment of threat intelligence and cyber forensics capacities in modern networks is sustainability.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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