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Embedded Intrusion Detection in IOT


   Digtial Age Research Centre

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  Dr Elisabeth Oswald  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

You will conduct research towards a distributed intrusion detection system for Edge devices in IoT applications suitable for real-world constrained embedded devices in realistic adversarial conditions. The intrusion detection system (IDS) you will develop will facilitate timely detection and containment of a security breach in the Edge, helping to make the Internet of Things (IoT) applications of tomorrow secure and reliable.

Your activity will be at an exciting intersection of the following fields:

  • Embedded development. The constrained nature of low-power embedded world will present you with stimulating research challenges. You will implement and test your results on real-world, low-power embedded HW platforms, maintaining a steady link between your research and practice and ensuring a real-world impact.
  • Applied security. To defend from attacks, you will get intimately familiar with them. You will acquire knowledge of different types of intrusion, how they manage to penetrate a system, and how they can be recognized.
  • Artificial intelligence. Modern IDS systems rely on AI. You will review the state of the art, select the most viable AI algorithms for an IDS in the constrained setting of IoT Edge, and carefully tweak them for the job.
  • Distributed computing. A swarm of Things in the Edge can, collaboratively monitor itself much more effectively than a single device. You will combine all the above and deploy a distributed IDS on a group of constrained embedded devices, identifying the underlying tradeoffs between efficiency and overhead.

The result of your work will be an IDS system, which will be able to make a difference in the security and reliability of real-world IoT applications.

We are looking for a student who has a Masters (or equivalent) degree in Electrical Engineering, Electronics or Computer Science with background and passion in (most of) the following topics:

  • Solid understanding of machine learning concepts and some practice
  • Proficiency with programming in C
  • Experience with embedded development is an advantage
  • Background in applied cryptography and security is an advantage
  • Fluency in English is required, proficiency in French is an advantage
  • Good communication and interpersonal skills

Funding Notes

The position is funded via CSEM, and you will be based in part at their premises (Switzerland), and in part at the Cybersecurity Research Group at AAU (Austria). This means you need to be eligible to work in Europe, and you need to be flexible because you will travel regularly.
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