Dirichlet-Based Trust Management for Effective Collaborative Intrusion Detection Networks
Why this work is in the frame
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Bibliographic record
Abstract
The accuracy of detecting intrusions within a Collaborative Intrusion Detection Network (CIDN) depends on the efficiency of collaboration between peer Intrusion Detection Systems (IDSes) as well as the security itself of the CIDN. In this paper, we propose Dirichlet-based trust management to measure the level of trust among IDSes according to their mutual experience. An acquaintance management algorithm is also proposed to allow each IDS to manage its acquaintances according to their trustworthiness. Our approach achieves strong scalability properties and is robust against common insider threats, resulting in an effective CIDN. We evaluate our approach based on a simulated CIDN, demonstrating its improved robustness, efficiency and scalability for collaborative intrusion detection in comparison with other existing models.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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