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Secure Autonomous Systems

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Secure Autonomous Systems

This course explores the security challenges and design principles unique to autonomous systems, with a particular focus on vehicles and drones. Students examine the constraints and vulnerabilities of core sensing and perception technologies — including camera and LiDAR processing pipelines — and analyse the security implications of both wired and wireless communication systems used in autonomous environments.

Main Topics
  • Fundamental Material on autonomous systems
  • Threat Modelling
  • Camera Sensor Processing Pipelines
  • Lidar Sensor Processing Pipelines
  • Lidar Sensor Processing Pipelines
  • Sensor Fusion, Collaborative Perception Systems
  • Wired and wireless communication in the context of autonomous vehicles
  • Reactive and Preventative Security

The General Competences that students should have acquired include:

  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Adapting to new situations
  • Decision-making
  • Working independently

Based on the above, upon completion of the course, students are expected to be able to:

  • Decompose and analyse the security state of typical autonomous systems
  • Identify, model, and investigate threats to typical sensor data processing pipelines (LiDAR + camera) using frameworks such as EVITA.
  • Identify, investigate, and mitigate vulnerabilities in in-vehicle networks (CAN, LIN, Automotive Ethernet).
  • Critically analyse novel attacks against camera- and Lidar-based system environments and propose possible mitigations.

The course Secure Autonomous Systems introduces students to the security challenges and design principles of autonomous systems, with focus on the inherent constraints and security considerations of the underlying sensing and perception technologies, i.e. Camera and Lidar sensors and their processing pipelines. Additionally, the typically utilized wired and wireless communication technologies and the resulting security implications for autonomous systems are discussed.

 

By combining theoretical teaching elements with practical exercises, students are, upon successful completion of the course, capable of the following:

  • Recognize and understand typical security issues of both conventional, as well as autonomous vehicle systems.
  • Demonstrate knowledge about the security state of current sensing/perception pipelines utilized in autonomous systems.
  • Understand both reactive and preventative security mechanisms for autonomous vehicles.
  • Understand typical vulnerabilities in both wired and wireless communication systems of autonomous systems and surrounding infrastructure.
  • Critically analyse and communicate current research gaps in the field of autonomous systems and corresponding infrastructure.

 

Students will be able to:

  • Evaluate safety-critical implications of cyber-physical attacks.
  • Monitor and contribute to ongoing research trends in autonomous system security.
  • Coordinate cross-domain teams (embedded, RF, AI) to deliver secure autonomous vehicle designs.
  • Provide strategic guidance on regulatory trends (UNECE WP.29, ISO 21434).
  • Drive security-by-design culture in agile automotive development lifecycles.