Technical report
Counter-UAS Attack and Defense Characterization in Autonomous Drone Swarms: A Kinematic Simulation Study
TL;DR
- 11,340 seeded trials across 4 attack classes and 6 defenses in a 4-drone warehouse swarm.
- Mission-success rate is the wrong primary C-UAS metric — physical effects are measurable even when task completion is unaffected (79.5% PN capture, 5–8 m position error).
- A kinematic plausibility detector achieves 39.8% true-positive rate at 0% false positives.
| Seeded trials | 11,340 |
|---|---|
| Attack classes | 4 |
| Matched defenses | 6 |
| PN capture rate | 79.5% |
| Detector | 39.8% TP @ 0% FP |
Abstract
11,340 seeded trials across four attack classes (GNSS spoofing, RF jamming, kinetic interception, control takeover) and six matched defenses in a four-drone warehouse swarm. Central finding: mission success rate is the wrong primary metric for C-UAS — physical effects (79.5% PN capture rate, 5–8 m position error) are clearly measurable even when aggregate task completion is unaffected. A kinematic plausibility detector achieves 39.8% TP at 0% false-positive rate. Includes an explicit fidelity boundary analysis delineating what kinematic simulation can and cannot faithfully reproduce.
Counter-UAS research has a measurement problem. The natural metric for evaluating an autonomous swarm — mission success rate — turns out to be almost useless for characterizing attack severity. This paper documents why, and what to measure instead.
The measurement problem
Suppose you inject a GNSS spoofing attack into a four-drone warehouse swarm. One drone flies to the wrong location. The other three compensate dynamically — redundant coverage, rerouted task assignments. Mission success rate: 94%. Does that mean the attack failed? No. A drone flew to the wrong place. That is an exploitable physical effect, and mission-success rate hid it.
This is the problem across all four attack classes we study: GNSS spoofing, RF jamming, kinetic interception, and control takeover. Swarm redundancy is a feature for resilience and a bug for C-UAS measurement — it absorbs attacks at the mission level while the physical effects of those attacks remain clearly measurable.
Experimental setup
11,340 seeded trials. Four attack classes, six matched defenses, run in a four-drone warehouse swarm with controlled task load. Seeded design means each trial has a fixed random seed, so we can compare attack vs. no-attack on identical swarm trajectories. This controls for the variance in swarm behavior that would otherwise obscure small attack effects.
The swarm performs a coverage task: inspect every zone in the warehouse. We measure mission success (did it cover everything?), time to completion, and — critically — the physical traces of each attack type.
Physical-effects metrics
For each attack class, we track the metric that directly captures the attack's physical signature:
- GNSS spoofing: position error — how far did the affected drone fly from its intended location? Median: 5–8 m under active spoofing.
- Kinetic interception: proportional navigation (PN) capture rate — what fraction of intercept attempts achieved physical proximity? 79.5% in our trials.
- RF jamming: control latency and packet loss rate during jamming windows.
- Control takeover: trajectory divergence from intended path post-takeover.
Kinematic detection
We also evaluate a passive kinematic plausibility detector — a module that watches each drone's trajectory and flags motion that is inconsistent with normal navigation physics (impossible accelerations, heading reversals inconsistent with the task, velocity spikes above the flight controller's limits).
The detector achieves a 39.8% true-positive rate at a 0% false-positive rate. That is not a high detection rate. But it is a 0% FP rate, which means every flag the detector raises is a real anomaly. In a security context, 0% FP is often more operationally useful than higher TP with non-zero FP — a system operator can act on every alert without alert fatigue.
Fidelity boundary
We include an explicit fidelity boundary section that delineates what kinematic simulation can and cannot faithfully reproduce for C-UAS work. RF propagation, antenna patterns, and electronic warfare effects are not modeled. The kinematic outcomes (trajectory under attack, physical interception geometry) are modeled. Readers should weight the RF-jamming results less heavily than the kinetic and GNSS results.
Full discussion in the companion blog post: We attacked our own drones 11,340 times.