Uncertainty-Aware Drone Swarm Disruption for Threat Mitigation
Bibliographic record
Abstract
This study tackles the challenge of neutralizing a malicious drone swarm targeting critical infrastructure. Given the impracticality of destroying all drones, we propose an uncertainty-aware threat assessment method, modeling payload size and positional uncertainties probabilistically. We studynode removalas the core decision problem to fragment communication and reduce attack power. The threat of each drone is quantified by its expected payload and distance-based probabilistic model. We also assess the probabilistic connectivity between drones to evaluate the swarm’s robustness. A novel evaluation function integrates these factors, and we introduce a probabilistic greedy search algorithm to minimize the swarm’s threat. We evaluate against centrality-based and weight-augmented dismantling heuristics, a recent DQN policy, and a small-n brute-force oracle. Our approach achieves a 70% reduction in attack power and near-optimal performance, deviating by less than 5% from the best possible outcomes under uncertainty.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".