Fighting the Locusts: Implementing Military Countermeasures Against Drones and Drone Swarms
Bibliographic record
Abstract
The use of unmanned aerial vehicles (UAVs) or “drones” in military contexts has skyrocketed in the last two decades, with missions ranging from surveillance, reconnaissance, and intelligence to combat support. Technological advances have led to an increase in drone capabilities and reliability, on the one hand, and to a decrease of production costs, on the other hand. Furthermore, drone availability has also drastically increased, and equipment that was once the exclusive privilege of a few countries can now be obtained by all national armed forces – and, as evidenced by recent attacks, by non-official forces. In this context, drones can become part of any conflict, and military strategists have to include response to drones and to potential drone swarms in their operational scenarios. Therefore, defense against drones has to become a component of any full-fledged military strategy. This analysis explores the conceptual and operational changes for military forces triggered by the massive emergence of drones, including the theoretical and practical challenges related to training and implementing specific anti-drone units. First, the evolution of the threats related to drones and drone swarms is identified. We then summarize the different possible countermeasures. Finally, we propose practical solutions to deploy these countermeasures, notably by exploring the possibilities of development and deployment of specialized anti-drone units and examining some of the challenges associated with fighting high-tech unmanned enemies rather than fighting soldiers in conventional battlefields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".