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 study <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">node removal</i> as 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".