UAVCAN Dataset Description

12/19/2022

∙

We collected attack data from unmanned vehicles using the UAVCAN protocol, and public and described technical documents. A testbed was built with a drone using PX4, and a total of three attacks, Flooding, Fuzzy, and Replay, were performed. The attack was carried out in a total of 10 scenarios. We expect that the attack data will help develop technologies such as anomaly detection to solve the security threat problem of drones.

READ FULL TEXT

UAVCAN Dataset Description

Behind The Wings: The Case of Reverse Engineering and Drone Hijacking in DJI Enhanced Wi-Fi Protocol

Security Analysis and Fault Detection Against Stealthy Replay Attacks

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

A Triangular Fuzzy based Multicriteria Decision Making Approach for Assessing Security Risks in 5G Networks

Anomaly Detection-Based Unknown Face Presentation Attack Detection

BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection

Adversarial Robustness of Graph-based Anomaly Detection

UAVCAN Dataset Description

Related Research

Behind The Wings: The Case of Reverse Engineering and Drone Hijacking in DJI Enhanced Wi-Fi Protocol

Security Analysis and Fault Detection Against Stealthy Replay Attacks

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

A Triangular Fuzzy based Multicriteria Decision Making Approach for Assessing Security Risks in 5G Networks

Anomaly Detection-Based Unknown Face Presentation Attack Detection

BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection

Adversarial Robustness of Graph-based Anomaly Detection