Search result: Catalogue data in Autumn Semester 2020

Computer Science Master Information
Master Studies (Programme Regulations 2020)
Majors
Major in Secure and Reliable Systems
Elective Courses
NumberTitleTypeECTSHoursLecturers
252-1411-00LSecurity of Wireless Networks Information W6 credits2V + 1U + 2AS. Capkun, K. Kostiainen
AbstractCore Elements: Wireless communication channel, Wireless network architectures and protocols, Attacks on wireless networks, Protection techniques.
ObjectiveAfter this course, the students should be able to: describe and classify security goals and attacks in wireless networks; describe security architectures of the following wireless systems and networks: 802.11, GSM/UMTS, RFID, ad hoc/sensor networks; reason about security protocols for wireless network; implement mechanisms to secure
802.11 networks.
ContentWireless channel basics. Wireless electronic warfare: jamming and target tracking. Basic security protocols in cellular, WLAN and
multi-hop networks. Recent advances in security of multi-hop networks; RFID privacy challenges and solutions.
263-2400-00LReliable and Interpretable Artificial Intelligence Information W6 credits2V + 2U + 1AM. Vechev
AbstractCreating reliable and explainable probabilistic models is a fundamental challenge to solving the artificial intelligence problem. This course covers some of the latest and most exciting advances that bring us closer to constructing such models.
ObjectiveThe main objective of this course is to expose students to the latest and most exciting research in the area of explainable and interpretable artificial intelligence, a topic of fundamental and increasing importance. Upon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of problems.

To facilitate deeper understanding, an important part of the course will be a group hands-on programming project where students will build a system based on the learned material.
ContentThe course covers some of the latest research (over the last 2-3 years) underlying the creation of safe, trustworthy, and reliable AI (more information here: Link):

* Adversarial Attacks on Deep Learning (noise-based, geometry attacks, sound attacks, physical attacks, autonomous driving, out-of-distribution)
* Defenses against attacks
* Combining gradient-based optimization with logic for encoding background knowledge
* Complete Certification of deep neural networks via automated reasoning (e.g., via numerical abstractions, mixed-integer solvers).
* Probabilistic certification of deep neural networks
* Training deep neural networks to be provably robust via automated reasoning
* Understanding and Interpreting Deep Networks
* Probabilistic Programming
Prerequisites / NoticeWhile not a formal requirement, the course assumes familiarity with basics of machine learning (especially probability theory, linear algebra, gradient descent, and neural networks). These topics are usually covered in “Intro to ML” classes at most institutions (e.g., “Introduction to Machine Learning” at ETH).

For solving assignments, some programming experience in Python is excepted.
227-0579-00LHardware Security Restricted registration - show details W6 credits4GK. Razavi
AbstractThis course covers the security of commodity computer hardware (e.g., CPU, DRAM, etc.) with a special focus on cutting-edge hands-on research. The aim of the course is familiarizing the students with hardware security and more specifically microarchitectural and circuit-level attacks and defenses through lectures, reviewing and discussing papers, and executing some of these advanced attacks.
ObjectiveBy the end of the course, the students will be familiar with the state of the art in commodity computer hardware attacks and defenses. More specifically, the students will learn about:

- security problems of commodity hardware that we use everyday and how you can defend against them.
- relevant computer architecture and operating system aspects of these issues.
- hands-on techniques for performing hardware attacks.
- writing critical reviews and constructive discussions with peers on this topic.

This is the course where you get credit points by building some of the most advanced exploits on the planet! The luckiest team will collect a Best Demo Award at the end of the course.
LiteratureSlides, relevant literature and manuals will be made available during the course.
Prerequisites / NoticeKnowledge of systems programming and computer architecture is a plus.
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