Dynamic Context-Aware Security in a Tactical Network Using Attribute-Based Encryption
Why this work is in the frame
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Bibliographic record
Abstract
The use of context-aware environmental monitoring systems is growing in several areas. However, the tactical edge networks present an extremely challenging and heterogeneous networking environment due to the complexity of the dynamic topologies of the battlefields. Installed devices in battlefields can be used to guarantee system performance and interoperability in such continuously varying network conditions. This work presented a Dynamic Context-Aware Security model in a Tactical Network that uses the data collected by the IoT sensors to provide automated policies for the dissemination of network state information. Moreover, our model leverages Attribute-Based Encryption (ABE) to ensure data security, hence secured communication. The proposed approach is validated through experiments in real-time. The achieved results show the effectiveness of our model in reducing the communication overhead associated with varying network conditions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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