Suchergebnis: Katalogdaten im Herbstsemester 2020

Cyber Security Master Information
Vertiefungsgebiet
Kernfächer
NummerTitelTypECTSUmfangDozierende
252-0463-00LSecurity Engineering Information W7 KP2V + 2U + 2AD. Basin, S. Krstic
KurzbeschreibungSubject of the class are engineering techniques for developing secure systems. We examine concepts, methods and tools, applied within the different activities of the SW development process to improve security of the system. Topics: security requirements&risk analysis, system modeling&model-based development methods, implementation-level security, and evaluation criteria for secure systems
LernzielSecurity engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.

The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.

Topics covered include

* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems
InhaltSecurity engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.

The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.

Topics covered include

* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems

Modules taught:

1. Introduction
- Introduction of Infsec group and speakers
- Security meets SW engineering: an introduction
- The activities of SW engineering, and where security fits in
- Overview of this class
2. Requirements Engineering: Security Requirements and some Analysis
- overview: functional and non-functional requirements
- use cases, misuse cases, sequence diagrams
- safety and security
- FMEA, FTA, attack trees
3. Modeling in the design activities
- structure, behavior, and data flow
- class diagrams, statecharts
4. Model-driven security for access control (design)
- SecureUML as a language for access control
- Combining Design Modeling Languages with SecureUML
- Semantics, i.e., what does it all mean,
- Generation
- Examples and experience
5. Model-driven security (Part II)
- Continuation of above topics
6. Security patterns (design and implementation)
7. Implementation-level security
- Buffer overflows
- Input checking
- Injection attacks
8. Testing
- overview
- model-based testing
- testing security properties
9. Risk analysis and management 1 (project management)
- "risk": assets, threats, vulnerabilities, risk
- risk assessment: quantitative and qualitative
- safeguards
- generic risk analysis procedure
- The OCTAVE approach
10. Risk analysis: IT baseline protection
- Overview
- Example
11. Evaluation criteria
- CMMI
- systems security engineering CMM
- common criteria
12. Guest lecture
- TBA
Literatur- Ross Anderson: Security Engineering, Wiley, 2001.
- Matt Bishop: Computer Security, Pearson Education, 2003.
- Ian Sommerville: Software Engineering, 6th ed., Addison-Wesley, 2001.
- John Viega, Gary McGraw: Building Secure Software, Addison-Wesley, 2002.
- Further relevant books and journal/conference articles will be announced in the lecture.
Voraussetzungen / BesonderesPrerequisite: Class on Information Security
252-1414-00LSystem Security Information W7 KP2V + 2U + 2AS. Capkun, A. Perrig
KurzbeschreibungThe first part of the lecture covers individual system aspects starting with tamperproof or tamper-resistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for building secure systems.
LernzielIn this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems, and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met.
InhaltThe first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc.

In the second part, the focus is on system design and methodologies for building secure systems. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, language-supported security, logging and auditing (BSM audit, dtrace, ...), cryptographic support, and trustworthy computing (TCG, SGX).

Along the lectures, model cases will be elaborated and evaluated in the exercises.
263-4640-00LNetwork Security Information W8 KP2V + 2U + 3AA. Perrig, S. Frei, M. Legner
KurzbeschreibungSome of today's most damaging attacks on computer systems involve
exploitation of network infrastructure, either as the target of attack
or as a vehicle to attack end systems. This course provides an
in-depth study of network attack techniques and methods to defend
against them.
Lernziel- Students are familiar with fundamental network security concepts.
- Students can assess current threats that Internet services and networked devices face, and can evaluate appropriate countermeasures.
- Students can identify and assess known vulnerabilities in a software system that is connected to the Internet (through analysis and penetration testing tools).
- Students have an in-depth understanding of a range of important security technologies.
- Students learn how formal analysis techniques can help in the design of secure networked systems.
InhaltThe course will cover topics spanning five broad themes: (1) network
defense mechanisms such as secure routing protocols, TLS, anonymous
communication systems, network intrusion detection systems, and
public-key infrastructures; (2) network attacks such as denial of
service (DoS) and distributed denial-of-service (DDoS) attacks; (3)
analysis and inference topics such as network forensics and attack
economics; (4) formal analysis techniques for verifying the security
properties of network architectures; and (5) new technologies related
to next-generation networks.
Voraussetzungen / BesonderesThis lecture is intended for students with an interest in securing
Internet communication services and network devices. Students are
assumed to have knowledge in networking as taught in a Communication
Networks lecture. The course will involve a course project and some
smaller programming projects as part of the homework. Students are
expected to have basic knowledge in network programming in a
programming language such as C/C++, Go, or Python.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
227-0575-00LAdvanced Topics in Communication Networks (Autumn 2020) Information W6 KP2V + 2UL. Vanbever
KurzbeschreibungThis course covers advanced topics and technologies in computer networks, both theoretically and practically. It is offered each Fall semester, with rotating topics. Repetition for credit is possible with consent of the instructor. In the Fall 2020, the course will cover advanced topics in Internet routing and forwarding.
LernzielThe goals of this course is to provide students with a deeper understanding of the existing and upcoming Internet routing and forwarding technologies used in large-scale computer networks such as Internet Service Providers (e.g., Swisscom or Deutsche Telekom), Content Delivery Networks (e.g., Netflix) and Data Centers (e.g., Google). Besides covering the fundamentals, the course will be “hands-on” and will enable students to play with the technologies in realistic network environments, and even implement some of them on their own during labs and a final group project.
InhaltThe course will cover advanced topics in Internet routing and forwarding such as:

- Tunneling
- Hierarchical routing
- Traffic Engineering and Load Balancing
- Virtual Private Networks
- Quality of Service/Queuing/Scheduling
- IP Multicast
- Fast Convergence
- Network virtualization
- Network programmability (OpenFlow, P4)
- Network measurements

The course will be divided in two main blocks. The first block (~10 weeks) will interleave classical lectures with practical exercises and labs. The second block (~4 weeks) will consist of a practical project which will be performed in small groups (~3 students). During the second block, lecture slots will be replaced by feedback sessions where students will be able to ask questions and get feedback about their project. The last week of the semester will be dedicated to student presentations and demonstrations.
SkriptLecture notes and material will be made available before each course on the course website.
LiteraturRelevant references will be made available through the course website.
Voraussetzungen / BesonderesPrerequisites: Communication Networks (227-0120-00L) or equivalents / good programming skills (in any language) are expected as both the exercices and the final project will involve coding.
227-0579-00LHardware Security Belegung eingeschränkt - Details anzeigen W6 KP4GK. Razavi
KurzbeschreibungThis 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.
LernzielBy 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.
LiteraturSlides, relevant literature and manuals will be made available during the course.
Voraussetzungen / BesonderesKnowledge of systems programming and computer architecture is a plus.
252-0811-00LApplied Security Laboratory Information
Gilt nur für MSc-Reglement 09: In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
W8 KP7PD. Basin
KurzbeschreibungHands-on course on applied aspects of information security. Applied
information security, operating system security, OS hardening, computer forensics, web application security, project work, design, implementation, and configuration of security mechanisms, risk analysis, system review.
LernzielThe Applied Security Laboratory addresses four major topics: operating system security (hardening, vulnerability scanning, access control, logging), application security with an emphasis on web applications (web server setup, common web exploits, authentication, session handling, code security), computer forensics, and risk analysis and risk management.
InhaltThis course emphasizes applied aspects of Information Security. The students will study a number of topics in a hands-on fashion and carry out experiments in order to better understand the need for secure implementation and configuration of IT systems and to assess the effectivity and impact of security measures. This part is based on a book and virtual machines that include example applications, questions, and answers.

The students will also complete an independent project: based on a set of functional requirements, they will design and implement a prototypical IT system. In addition, they will conduct a thorough security analysis and devise appropriate security measures for their systems. Finally, they will carry out a technical and conceptual review of another system. All project work will be performed in teams and must be properly documented.
SkriptThe course is based on the book "Applied Information Security - A Hands-on Approach". More information: http://www.infsec.ethz.ch/appliedlabbook
LiteraturRecommended reading includes:
* Pfleeger, Pfleeger: Security in Computing, Third Edition, Prentice Hall, available online from within ETH
* Garfinkel, Schwartz, Spafford: Practical Unix & Internet Security, O'Reilly & Associates.
* Various: OWASP Guide to Building Secure Web Applications, available online
* Huseby: Innocent Code -- A Security Wake-Up Call for Web Programmers, John Wiley & Sons.
* Scambray, Schema: Hacking Exposed Web Applications, McGraw-Hill.
* O'Reilly, Loukides: Unix Power Tools, O'Reilly & Associates.
* Frisch: Essential System Administration, O'Reilly & Associates.
* NIST: Risk Management Guide for Information Technology Systems, available online as PDF
* BSI: IT-Grundschutzhandbuch, available online
Voraussetzungen / Besonderes* The lab allows flexible working since there are only few mandatory meetings during the semester.
* The lab covers a variety of different techniques. Thus, participating students should have a solid foundation in the following areas: information security, operating system administration (especially Unix/Linux), and networking. Students are also expected to have a basic understanding of HTML, PHP, JavaScript, and MySQL because several examples are implemented in these languages.
* Students must be prepared to spend more than three hours per week to complete the lab assignments and the project. This applies particularly to students who do not meet the recommended requirements given above. Successful participants of the course receive 8 credits as compensation for their effort.
* All participants must sign the lab's charter and usage policy during the introduction lecture.
252-1411-00LSecurity of Wireless Networks Information W6 KP2V + 1U + 2AS. Capkun, K. Kostiainen
KurzbeschreibungCore Elements: Wireless communication channel, Wireless network architectures and protocols, Attacks on wireless networks, Protection techniques.
LernzielAfter 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.
InhaltWireless 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.
Seminar
NummerTitelTypECTSUmfangDozierende
252-4601-00LCurrent Topics in Information Security Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 24.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 KP2SS. Capkun, K. Paterson, A. Perrig
KurzbeschreibungThe seminar covers various topics in information security: security protocols (models, specification & verification), trust management, access control, non-interference, side-channel attacks, identity-based cryptography, host-based attack detection, anomaly detection in backbone networks, key-management for sensor networks.
LernzielThe main goals of the seminar are the independent study of scientific literature and assessment of its contributions as well as learning and practicing presentation techniques.
InhaltThe seminar covers various topics in information security, including network security, cryptography and security protocols. The participants are expected to read a scientific paper and present it in a 35-40 min talk. At the beginning of the semester a short introduction to presentation techniques will be given.

Selected Topics

- security protocols: models, specification & verification
- trust management, access control and non-interference
- side-channel attacks
- identity-based cryptography
- host-based attack detection
- anomaly detection in backbone networks
- key-management for sensor networks
LiteraturThe reading list will be published on the course web site.
Semesterprojekt
NummerTitelTypECTSUmfangDozierende
260-0100-00LSemester Project Belegung eingeschränkt - Details anzeigen
Only for Cyber Security MSc
W12 KP26AProfessor/innen
KurzbeschreibungThe Semester Project provides students with the opportunity to apply acquired knowledge and skills.
LernzielThe students can gain hand-on experience by solving independently a technical-scientific problem.
Voraussetzungen / BesonderesPrerequisites: At least one core course in Cyber Security and one inter focus course must have been completed successfully.
Ergänzung
Computational Science
Kernfächer
NummerTitelTypECTSUmfangDozierende
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
636-0007-00LComputational Systems Biology Information W6 KP3V + 2UJ. Stelling
KurzbeschreibungStudy of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).
LernzielThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
InhaltBiology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.
Skripthttp://www.csb.ethz.ch/education/lectures.html
LiteraturU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010.

B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013
Wahlfächer
NummerTitelTypECTSUmfangDozierende
252-0543-01LComputer Graphics Information W8 KP3V + 2U + 2AM. Gross, M. Papas
KurzbeschreibungThis course covers some of the fundamental concepts of computer graphics generation of photorealistic images from digital representations of 3D scenes and image-based methods for recovering digital scene representations from captured images.
LernzielAt the end of the course the students will be able to build a rendering system. The students will study the basic principles of rendering and image synthesis. In addition, the course is intended to stimulate the students' curiosity to explore the field of computer graphics in subsequent courses or on their own.
InhaltThis course covers fundamental concepts of modern computer graphics. Students will learn about 3D object representations and the details of how to generate photorealistic images from digital representations of 3D scenes. Starting with an introduction to 3D shape modeling, geometry representation and texture mapping, we will move on to the physics of light transport, acceleration structures, appearance modeling and Monte Carlo integration. We will apply these principles for computing light transport of direct and global illumination due to surfaces and participating media. We will end with an overview of modern image-based capture and image synthesis methods, covering topics such as geometry and material capture, light-fields and depth-image based rendering.
Skriptno
LiteraturBooks:
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting
Multiple view geometry in computer vision
Physically Based Rendering: From Theory to Implementation
Voraussetzungen / BesonderesPrerequisites:
Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Visual Computing course recommended.
The programming assignments will be in C++. This will not be taught in the class.
261-5100-00LComputational Biomedicine Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 60.
W5 KP2V + 1U + 1AG. Rätsch, V. Boeva, N. Davidson
KurzbeschreibungThe course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
LernzielOver the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.
InhaltThe course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine:
1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
Voraussetzungen / BesonderesData Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
636-0017-00LComputational BiologyW6 KP3G + 2AT. Stadler, T. Vaughan
KurzbeschreibungThe aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.
LernzielAttendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are:
* stochastic models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
Attendees will apply these concepts to a number of applications yielding biological insight into:
* epidemiology
* pathogen evolution
* macroevolution of species
InhaltThe course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.
SkriptLecture slides will be available on moodle.
LiteraturThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
Voraussetzungen / BesonderesBasic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date http://www.cbb.ethz.ch/news-events.html
For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course.
Distributed Systems
Kernfächer
NummerTitelTypECTSUmfangDozierende
252-1414-00LSystem Security Information W7 KP2V + 2U + 2AS. Capkun, A. Perrig
KurzbeschreibungThe first part of the lecture covers individual system aspects starting with tamperproof or tamper-resistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for building secure systems.
LernzielIn this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems, and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met.
InhaltThe first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc.

In the second part, the focus is on system design and methodologies for building secure systems. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, language-supported security, logging and auditing (BSM audit, dtrace, ...), cryptographic support, and trustworthy computing (TCG, SGX).

Along the lectures, model cases will be elaborated and evaluated in the exercises.
263-3845-00LData Management Systems Information W8 KP3V + 1U + 3AG. Alonso
KurzbeschreibungThe course will cover the implementation aspects of data management systems using relational database engines as a starting point to cover the basic concepts of efficient data processing and then expanding those concepts to modern implementations in data centers and the cloud.
LernzielThe goal of the course is to convey the fundamental aspects of efficient data management from a systems implementation perspective: storage, access, organization, indexing, consistency, concurrency, transactions, distribution, query compilation vs interpretation, data representations, etc. Using conventional relational engines as a starting point, the course will aim at providing an in depth coverage of the latest technologies used in data centers and the cloud to implement large scale data processing in various forms.
InhaltThe course will first cover fundamental concepts in data management: storage, locality, query optimization, declarative interfaces, concurrency control and recovery, buffer managers, management of the memory hierarchy, presenting them in a system independent manner. The course will place an special emphasis on understating these basic principles as they are key to understanding what problems existing systems try to address. It will then proceed to explore their implementation in modern relational engines supporting SQL to then expand the range of systems used in the cloud: key value stores, geo-replication, query as a service, serverless, large scale analytics engines, etc.
LiteraturThe main source of information for the course will be articles and research papers describing the architecture of the systems discussed. The list of papers will be provided at the beginning of the course.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
252-0817-00LDistributed Systems Laboratory
Gilt nur für MSc-Reglement 09: Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Diese Labs gelten nur für das Masterstudium. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.
W10 KP9PG. Alonso, T. Hoefler, A. Klimovic, T. Roscoe, A. Singla, R. Wattenhofer, C. Zhang
KurzbeschreibungThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including wireless networks, ad-hoc networks, RFID, and distributed applications on smartphones.
LernzielGain hands-on-experience with real products and the latest technology in distributed systems.
InhaltThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on smartphones. The goal of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.
227-2210-00LComputer Architecture Information W8 KP6G + 1AO. Mutlu
KurzbeschreibungComputer architecture is the science & art of designing and optimizing hardware components and the hardware/software interface to create a computer that meets design goals. This course covers basic components of a modern computing system (processors, memory, interconnects, accelerators). The course takes a hardware/software cooperative approach to understanding and designing computing systems.
LernzielWe will learn the fundamental concepts of the different parts of modern computing systems, as well as the latest trends by exploring the recent research in Industry and Academia. We will extensively cover memory technologies (including DRAM and new Non-Volatile Memory technologies), memory scheduling, parallel computing systems (including multicore processors and GPUs), heterogeneous computing, processing-in-memory, interconnection networks, specialized systems for major data-intensive workloads (e.g. graph processing, bioinformatics, machine learning), etc.
InhaltThe principles presented in the lecture are reinforced in the laboratory through 1) the design and implementation of a cycle-accurate simulator, where we will explore different components of a modern computing system (e.g., pipeline, memory hierarchy, branch prediction, prefetching, caches, multithreading), and 2) the extension of state-of-the-art research simulators (e.g., Ramulator) for more in-depth understanding of specific system components (e.g., memory scheduling, prefetching).
SkriptAll the materials (including lecture slides) will be provided on the course website: https://safari.ethz.ch/architecture/
The video recordings of the lectures are expected to be made available after lectures.
LiteraturWe will provide required and recommended readings in every lecture. They will mainly consist of research papers presented in major Computer Architecture and related conferences and journals.
Voraussetzungen / BesonderesDigital Design and Computer Architecture.
263-3850-00LInformal Methods Information W5 KP2G + 2AD. Cock
KurzbeschreibungFormal methods are increasingly a key part of the methodological toolkit of systems programmers - those writing operating systems, databases, and distributed systems. This course is about how to apply concepts, techniques, and principles from formal methods to such software systems, and how to get into the habit of thinking formally about systems design even when writing low-level C code.
LernzielThis course is about equipping students whose focus is systems with the insights and conceptual tools provided by formal methods, and thereby enabling them to become better systems programmers.
By the end of the course, students should be able to seamlessly integrate basic concepts form formal methods into how they conceive, design, implement, reason about, and debug computer systems.

The goal is not to provide a comprehensive introduction to formal methods - this is well covered by other courses in the department. Instead, it is intended to provide students in computer systems (who may or may not have existing background knowledge of formal methods) with a basis for applying formal methods in their work.
InhaltThis course does not assume prior knowledge of formal methods, and will start with a quick review of topics such static vs. dynamic reasoning, variants and invariants, program algebra and refinement, etc. However, it is strongly recommended that students have already taken one of the introductory formal methods course at ETH (or equivalents elsewhere) before taking this course - the emphasis is on reinforcing these concepts by applying them, not to teach them from scratch.

Instead, the majority of the course will be about how to apply these techniques to actual, practical code in real systems. We will work from real systems code written both by students taking the course, and practical systems developed using formal techniques, in particular the verified seL4 microkernel will be a key case study. We will also focus on informal, pen-and-paper arguments for correctness of programs and systems rather than using theorem provers or automated verification tools; again these latter techniques are well covered in other courses (and recommended as a complement to this one).
Information Systems
Kernfächer
NummerTitelTypECTSUmfangDozierende
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
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