Autonomous Jam-Resistant Drone Mesh Defense with Claude and Daytona
In modern battlefield environments, drone swarms are increasingly targeted by sophisticated jamming and 'hypnosis' (confusing) systems. This challenge tasks you with building an autonomous network security agent using the Claude Agents SDK and Daytona. The agent will manage a simulated drone mesh network (using Mininet-WiFi or a custom ns-3 harness) to detect signal interference and execute countermeasures. The agent will run inside a Daytona-managed sandbox, giving it secure, isolated access to network monitoring tools. Using Claude's extended thinking capabilities, the agent must analyze real-time telemetry (SNR, packet loss, RSSI) to distinguish between natural noise and intentional jamming. Upon detection, it must orchestrate a 'frequency hop' or transition to a jam-resistant modulation scheme (like FHSS or DSSS) across the swarm without losing connectivity between nodes.
What you are building
The core problem, expected build, and operating context for this challenge.
In modern battlefield environments, drone swarms are increasingly targeted by sophisticated jamming and 'hypnosis' (confusing) systems. This challenge tasks you with building an autonomous network security agent using the Claude Agents SDK and Daytona. The agent will manage a simulated drone mesh network (using Mininet-WiFi or a custom ns-3 harness) to detect signal interference and execute countermeasures. The agent will run inside a Daytona-managed sandbox, giving it secure, isolated access to network monitoring tools. Using Claude's extended thinking capabilities, the agent must analyze real-time telemetry (SNR, packet loss, RSSI) to distinguish between natural noise and intentional jamming. Upon detection, it must orchestrate a 'frequency hop' or transition to a jam-resistant modulation scheme (like FHSS or DSSS) across the swarm without losing connectivity between nodes.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Detection Accuracy
The agent must correctly identify 90% of jamming events while ignoring random noise spikes.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Swarm Synchronization
All nodes in the mesh must switch to the new channel within 500ms of each other.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Packet Delivery Ratio (PDR)
Average PDR across the swarm during the 10-minute simulation attack window. • target: 0.85 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Claude Agents SDK by implementing tools that interface with Linux network namespaces and wireless tools (iw, ip link).
Implement Autonomous Monitoring by developing a streaming pipeline that feeds RSSI and noise-floor data into Claude's context.
Design Mitigation Strategies that utilize multi-agent coordination for synchronized frequency shifts across a drone swarm.
Orchestrate Environment Setup using Daytona to create a consistent developer and agent workspace with pre-installed simulation tools.
Deploy a jam-detection heuristic that leverages Claude's ability to correlate telemetry spikes across geographically dispersed nodes.
Integrate jam-resistant protocols by switching the simulated network's physical layer configuration via the agent's shell tools.
How this agent runs
Evaluation is based on the agent's success in maintaining network throughput in a high-jamming simulation.
Challenge input
CSV stream of network telemetry (timestamp, node_id, rssi, noise, pdr).
Claude Agents SDK
Superior 'thinking' capabilities for signal analysis.
Daytona
Provides clean, standardized sandboxes for network simulation.
Mininet-WiFi
Open-source tool for SDN and wireless mesh simulation.
Evaluated output
A series of shell commands executed by the agent to change channel and modulation.
- The agent must correctly identify 90% of jamming events while ignoring random noise spikes.
- All nodes in the mesh must switch to the new channel within 500ms of each other.
- Average PDR across the swarm during the 10-minute simulation attack window. • target: 0.85 • range: 0-1
- Benchmark: Drone-Net-Defense-v1
- Packet Delivery Ratio (PDR) target: 0.85
- Protected reference cases support private evaluation
- Docker (Daytona Sandbox) execution harness
- Docker sandbox
View technical recipe
Configured tools
- Claude Code · Required
- Claude Code · Required
- Claude 4.1 Opus · Optional
- Claude 4 Sonnet · Optional
Evaluation contract
- Detection Accuracy · Weight 1
- Swarm Synchronization · Weight 1
- Packet Delivery Ratio (PDR) · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsFind another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.