Results of the IAEA coordinated research project enhancing computer security for radiation detection systems
Bibliographic record
Abstract
This work presents results of the International Atomic Energy Agency’s Coordinated Research Project (CRP) on Enhancing Computer Security for Radiation Detection Systems. These systems include a wide range of fixed and mobile radiation detectors used in safety and security applications. The signals and data generated by radiation detection systems are transmitted to local or remote monitoring centers through various communication channels, enhancing the effectiveness of threat detection and enabling a timely response to alarm conditions. However, during the generation, processing, transmission and display of this information, data can be compromised. This CRP brought together 11 research institutes from 10 Member States to explore various topics, including threat modeling, cloud computing, malware propagation in large radiation detection networks, intrusion detection systems, defensive computer security architecture, wireless communication security, and simulation of integrated physical protection and radiation detection systems. The participating institutes created a reference architecture, designed models for synthetic radiation data and anomaly detection methods, performed vulnerability assessments of radiation detection systems, and developed prototypes for both virtual and hardware-in-the-loop testbeds. Their research also led to the creation of simulators, including a physical protection–radiation detector simulator that incorporates a 3D hospital model.
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.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".