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EC08 - Secure Autonomous Systems

A focused exploration of attacks, defences, and trust in autonomous systems.

Credits

7.5 ECTS

Semester

2 Semester

Delivery

Online

Duration

13 weeks

Language

English

About This Course

This is a second-semester course in the MBA in Advanced Cybersecurity Technologies &
Governance
IoT Security introduces students to the security challenges of modern Internet of Things systems, combining solid theoretical foundations with hands-on security practice. The course covers IoT architectures, threat modelling, cryptography, secure communication protocols, and web and network security in connected environments. Through practical penetration testing exercises and Capture the Flag challenges, students learn to identify vulnerabilities, exploit them responsibly, and design effective mitigation strategies. The course also engages with current research and real-world attack trends, preparing students to assess and strengthen the security of complex IoT deployments in professional settings.

What You Will Learn


Foundations of Autonomous System Security

  • Security challenges in modern autonomous and connected vehicle systems
  • Vulnerabilities in sensing, perception, and control pipelines
  • The relationship between safety, reliability, and cybersecurity in autonomous systems


Sensor, Communication & Infrastructure Security

  • Security considerations for camera and LiDAR perception pipelines
  • Threats affecting sensor fusion and collaborative perception environments
  • Vulnerabilities in in-vehicle networks and autonomous system communication infrastructures


Defensive Architectures for Autonomous Systems

  • Reactive and preventative security mechanisms for autonomous vehicles

  • Security risks in wired and wireless vehicle communication systems

  • Emerging research challenges and future security architectures for autonomous mobility

Your 13-Week Journey

Here’s how your learning unfolds

Week 1 – Fundamentals on Autonomous Systems

This lecture welcomes the students, outlines the courses organization and deadlines, and introduces the students to fundamental systematization of autonomous systems.

Week 2 – Threat Modelling

As basis for further discussion, this lecture introduces relevant threat modeling techniques for autonomous systems.

Week 3 –Camera Sensor Processing Pipeline

In this first sensor-specific lecture, typical camera sensor processing pipelines are introduced and the corresponding security implications highlighted.

Week 4 – Lidar sensor processing pipeline

In this second sensor-specific lecture, typical Lidar sensor processing pipelines are introduced and the corresponding security implications highlighted.

Week 5 – Sensor Fusion

This lecture introduces the students to current approaches and security considerations of autonomous systems relying on single- and multi-modality sensor fusion algorithms.

Week 6 – Collaborative Perception

In this lecture, students are introduced to collaborative perception algorithms and their inherent security implications when utilized in autonomous systems.

Week 7 – Intelligent Assets supporting autonomous systems in Smart Cities

This lecture analyzes and discusses the typically employed infrastructure enabling autonomous system operation in smart cities, with special focus on the underlying security considerations.

Week 8 – Wired communication infrastructure security

This lecture introduces the students to classical in-vehicle wired communication infrastructure and analyzes the typical security state of modern vehicles.

Week 9 – Wireless communication infrastructure security 

This lecture introduces students to often utilized wireless communication methods in the supporting infrastructure of autonomous vehicles and analyzes the resulting security considerations.

Week 10 – Reactive and Preventative Security

In the scope of this lecture, students are introduced to defensive methodologies protecting autonomous systems and their systematization.

Week 11 – Real-World defensive countermeasures for autonomous vehicles 

This lecture continues the defensive discussion by highlighting real-world defensive methodologies, as well as previously observed misbehavior of autonomous systems prevented by such defensive solutions.

Week 12 – Real-Time System Security

This lecture introduces the students to typical real-time system constraints and their security implications for autonomous systems.

Week 13 – Recap and Research Outlook 

In this final lecture, students recap the acquired knowledge and skills with all lecturers in a Q&A session.


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Skills You Will Gain

Autonomous System Threat Analysis

  • Analysing attacks against camera- and LiDAR-based perception systems
  • Evaluating safety implications of cyber-physical attacks on autonomous vehicles
  • Identifying vulnerabilities across sensing, processing, and control layers

Secure Architecture & Vulnerability Mitigation

  • Decomposing autonomous system architectures to assess security posture
  • Modelling threats to sensor pipelines using frameworks such as EVITA
  • Investigating vulnerabilities in in-vehicle networks such as CAN, LIN, and Automotive Ethernet

Strategic Security Leadership in Autonomous Mobility

  • Coordinating multidisciplinary teams to design secure autonomous systems
  • Monitoring emerging research trends and regulatory developments
  • Promoting security-by-design practices within automotive development lifecycles

Continue Your Journey