AI-Powered Drone Threat Detection
Develop an AI system to detect and classify potential threats posed by drones, focusing on anomaly detection in flight patterns and payload analysis. The system should ingest diverse data sources, including publicly available flight data, news reports, and social media posts, to build a comprehensive risk assessment model. The goal is to create a robust, scalable, and explainable system that can assist in mitigating potential risks associated with unauthorized drone activity. This challenge focuses on practical implementation and utilizes readily available data sources to develop a valuable tool for security professionals.
What you are building
Build an AI system to detect and classify threats posed by DJI drones using diverse data sources.
How work is evaluated
Evaluation will be based on the accuracy, precision, recall, and F1-score of the threat detection model, as well as the usability and scalability of the system.
Datasets
Public datasets, plus the private datasets you have access to.
Learning goals
Build a user-friendly interface for visualizing results and generating reports.
Evaluate the model's performance using appropriate metrics.
Create a classification model to categorize potential threats.
Develop a robust data pipeline for ingesting and processing diverse data sources.
Implement anomaly detection algorithms to identify suspicious drone behavior.
Resources
Sample data for 1 tasks
Challenge setup
Evaluation will be based on the accuracy, precision, recall, and F1-score of the threat detection model, as well as the usability and scalability of the system.
Challenge input
JSON: { 'flight_data': [..], 'payload_data': [..] }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON: { 'prediction': 'Threat' or 'No Threat', 'confidence': 0.0-1.0 }
- No evaluation checks are configured.
- Accuracy target: 0.8
- Python execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- No evaluation checks are configured.
Metrics
- Accuracy · Target 0.8
- Precision · Target 0.7
- Recall · Target 0.7
Workflow tasks
- Task 1
Input: JSON: { 'flight_data': [..], 'payload_data': [..] }
Output: JSON: { 'prediction': 'Threat' or 'No Threat', 'confidence': 0.0-1.0 }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.