ROS-Defender: SDN-based Security Policy Enforcement for Robotic Applications
This academic paper proposes ROS-Defender, a holistic security architecture for Robot Operating System (ROS) based robotic systems. It integrates a security event management system, an intrusion prevention system, and a firewall using software-defined networking (SDN) to provide dynamic policy enforcement at both the network and topic levels. The approach protects robotic applications against attacks like denial of service and compromised nodes without requiring changes to existing ROS APIs.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Robotic software developers, cybersecurity researchers, and engineers looking to secure ROS-based robotic systems against network-level and node-level attacks.
What the document contains
The document describes the design, implementation, and evaluation of ROS-Defender, a security architecture for robotic applications built on the Robot Operating System (ROS). It covers threat modeling, SDN-based network filtering, anomaly detection via logs and network traffic, and a domain-specific policy language for defining topic-level access controls.
It does not provide commercial product warranty terms, legal liability frameworks for robotic malfunctions, or legislative mandates for robot deployment.
- ROS-Defender combines application-level and network-level anomaly detection with dynamic policy enforcement using software-defined networking for robotic systems.
- The proposed ROS-Policy-Language allows users to express access control rules using familiar ROS semantics and vocabulary like topics and services.
- The framework successfully addresses limitations in existing security solutions such as SROS, including the inability to react dynamically to compromised nodes at runtime.
- Experimental results demonstrate that ROS-Defender can be deployed on physical and virtual robotic systems with manageable latency and overhead.
· I. Introduction · II. Security of ROS · III. Adversarial Model · IV. ROS-Defender Design · V. Experimental Results · VI. Related Work · VII. Conclusion and Future Work
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-23
Privacy
1/5Protection of personal data and private life
Finally, it was shown how an attacker could compromise privacy by exploiting faulty APIs to obtain access to camera and microphones
Section I
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-23