Security issues and privacy breaches in modern systems (blockchains, datacenters, AI, etc.) result in billions of losses due to hacks and system downtime. This course introduces the security issues in such modern systems and covers state-of-the-art automated techniques (developed in the past few years) that can are applied to build more secure and reliable systems. The course has a practical focus and covers systems build by successful ETH spin-offs (ChainSecurity, which was acquired by PwC, and DeepCode).


  1. Learn about security issues in modern systems -- blockchains, smart contracts, data centers -- and why they are challenging to address.
  2. Understand how the latest automated analysis techniques work, both discrete and probabilistic.
  3. Understand how these techniques combine with machine-learning methods, both supervised and unsupervised.
  4. Understand how to use these methods to build reliable and secure modern systems.
  5. Learn about new open problems that if solved can lead to research and commercial impact.

Part I: Security of Blockchains

  • We will cover existing blockchains (e.g., Ethereum, Bitcoin), how they work, what the core security issues are, and how these have led to massive financial losses.
  • We will show how to extract useful information about smart contracts and transactions using interactive analysis frameworks for querying blockchains (e.g. Google's Ethereum BigQuery).
  • We will discuss the state-of-the-art security tools (e.g., Securify) for ensuring that smart contracts are free of security vulnerabilities.
  • We will study the latest automated reasoning systems (e.g., VerX) for checking custom (temporal) properties of smart contracts and illustrate their operation on real-world use cases.

Part II: Security of Datacenters and Networks

  • We will show how to ensure that datacenters and ISPs are secured using declarative reasoning methods (e.g., Datalog). We will also see how to automatically synthesize secure configurations (e.g. using SyNET and NetComplete) which lead to desirable behaviors, thus automating the job of the network operator and avoiding critical errors.
  • We will discuss how to apply modern discrete probabilistic inference (e.g., PSI and Bayonet) so to reason about probabilistic network properties (e.g., the probability of a packet reaching a destination if links fail).

Part III: Machine Learning for Security

  • We will discuss how machine learning models for structured prediction are used to address security tasks, including de-obfuscation of binaries (DeBIN), Android APKs (DeGuard) and JavaScript (JSNice).
  • We will study to leverage program abstractions in combination with clustering techniques to learn security rules for cryptography APIs from large codebases.
  • We will study how to automatically learn to identify security vulnerabilities related to the handling of untrusted inputs (cross-Site scripting, SQL injection, path traversal, remote code execution) from large codebases.

Course Project

The course involve a hands-on programming project where the methods studied in the class will be applied. You can work on the project in a group consisting of at most 2 students. Registration is closed.

The description of the course project can be found here.

Answers to common student questions can be found here.

Group registration6PM CEST, April 2, 2020
Project announcement6PM CEST, April 6, 2020
Preliminary deadline (optional)6PM CEST, May 17, 2020
Preliminary feedback 6PM CEST, May 25, 2020
Final deadline6PM CEST, May 31, 2020


No. DateContentSlides Exercises Solutions Recording
1 Feb 17 Introduction PDF No Exercise
2 Feb 24 Blockchain basics and Bitcoin PDF PDF PDF
3 Mar 02 Ethereum and smart contracts PDF PDF PDF
4 Mar 09 Fuzzing PDF PDF PDF
5 Mar 16 Datalog and static analysis PDF PDF PDF
6 Mar 23 Functional specification PDF PDF PDF CODE
7 Mar 30 Formal verification PDF PDF PDF
8 Apr 6 Formal verification wrap-up \& Project announcement PDF
9 Apr 27 Network verification PDF PDF PDF
10 May 4 Network synthesis PDF PDF PDF
11 May 11 Big code PDF No Exercise
12 May 18 Summary lecture PDF No Exercise

Past exams

The exam from last year is available here.