Embedded systems are specialized computing systems designed to perform dedicated functions within larger systems and are critical to modern infrastructure, from smart devices to industrial automation. Due to the pervasiveness of these systems, their security is crucial to ensure safety, privacy, and the reliability of critical technologies. In this course, we’ll dive into the latest topics in embedded systems security, with a focus on environments like automotive systems, industrial control, and robotics. You’ll get hands-on experience in developing and securing firmware, rehosting embedded devices, and digging into side-channel attacks. We’ll also cover the basics of software security, fault injection, and defense strategies designed specifically for embedded systems. Along the way, you’ll explore and analyze recent research through paper reviews and discussions, keeping up with the latest trends in the field. The course wraps up with a project where you’ll apply everything you’ve learned to real-world challenges and present your findings in a final showcase.
The schedule is tentative and may change. All code used in class will be made available on GitHub, if not already open-source.
Week 1: Intro to Embedded Systems
Weeks 1–3: Knowing the Hardware (Firmware Development)
Weeks 4–5: Software Security Fundamentals and Challenges in Embedded Systems
Weeks 6–7: Rehosting
Week 8: Midterm Presentations
Weeks 8-9: Fault Injection + Paper Survey
Weeks 10–12: Side Channel Analysis + Paper Survey
Weeks 13–15: Domain-Specific Defense + Paper Survey
Week 16: Current Trends in Embedded Systems Security
Week 18: Final Presentations
There are no exams for this course. Your grade will be decided based on your course project (70%) and paper presentations (30%). The course project will be evaluated based on the novelty of the project, the quality of the artifact, and the project report. Students should ensure the novelty of the project by doing a literature survey. Your grade will be severely impacted if there is existing work on the proposed idea. Moreover, artifact submission is necessary; however, it is fine to submit a Work-In-Progress, given appropriate justification.
Students are permitted to use Generative AI (GenAI) tools as part of their coursework. However, if a student chooses to do so, it is their responsibility to verify the accuracy of any information or claims produced by the AI. Any errors, hallucinations, or misleading outputs from such tools remain the sole responsibility of the student. For homework assignments, students are required to submit relevant chat logs or interactions with the GenAI system along with their work. These logs should clearly show how the tool was used in the completion of the assignment. Students should not rely on GenAI tools as an “answering oracle.” Instead, these tools are to be used as helpful assistants to support learning, research, and problem-solving efforts, not as a substitute for the student’s own understanding and work.
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