Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Search result: Catalogue data in Spring Semester 2019

Computational Science and Engineering Bachelor Information
For All Programme Regulations
In the ‘electives’ subcategory, at least two course units must be successfully completed.
151-0834-00LForming Technology II - Introduction Virtual Process Modelling Information W4 credits2V + 2UP. Hora
AbstractThe lecture imparts the principles of the nonlinear Finite-Element-Methods (FEM), implicit and explicit FEM-integration procedures for quasistatic applications, modeling of coupled thermo-mechanical problems, modeling of time dependent contact conditions, modeling of the nonlinear material behaviour, modeling of friction, FEM-based prediction of failure by means of cracks and crinkles.
ObjectiveProzess optimization through numerical methods
ContentApplication of virtual simulation methods for planning and optimization of metal-forming processes. Fundamentals of virtual simulation processes, based on Finite-Element-Methods (FEM) and Finite-Difference-Methods (FDM). Introduction to the basics of continuum and plasto mechanics to mathematically describe the plastic material flow of metals. The procedures to acquire process relevant features. The exercises include the application of industrial simulation tools for deep drawing in automotive applications, high pressure inner metal working (space frame) and rod extrusion.
Lecture notesyes
151-0836-00LVirtual Process Control in Forming Manufacturing Systems Information
Does not take place this semester.
W5 credits2V + 2UP. Hora
AbstractIntroduction to the methods of virtual modeling of manufacturing processes, illustrated with examples from the digital automotive plant and others. The lecture presents an opportunity to learn the application of non-linear finite element analysis and optimization methods and also adresses stochastical methods for the control of the robust processes.
ObjectiveIntegral study of virtual planning technologies in forming manufacturing systems
ContentIntroduction to the methods of digital plant modeling. Examples: digital automitive plant, digital space-frame manufacturing, digital extrusion plant. Methods: virtual modeling of complex forming processes, non-linear FEA, optimization methods, stochastical methods.
Lecture notesyes
151-3202-00LProduct Development and Engineering Design Restricted registration - show details
Number of participants limited to 60.
W4 credits2GK. Shea, T. Stankovic
AbstractThe course introduces students to the product development process. In a team, you will explore the early phases of conceptual development and product design, from ideation and concept generation through to hands-on prototyping. This is an opportunity to gain product development experience and improve your skills in prototyping and presenting your product ideas. The project topic changes each year.
ObjectiveThe course introduces you to the product development process and methods in engineering design for: product planning, user-centered design, creating product specifications, ideation including concept generation and selection methods, material selection methods and prototyping. Further topics include product lifecycle and sustainable design as well as design for manufacture, focusing on additive manufacture. You will actively apply the process and methods learned throughout the semester in a team on a product development project including hands-on prototyping.
ContentWeekly topics accompanying the product development project include:
1 Introduction to Product Development and Engineering Design
2 Product Planning and Social-Economic-Technology (SET) Factors
3 User-Centered Design and Product Specification
4 Concept Generation and Selection Methods
5 System Design and Embodiment Design
6 Hands-On Prototyping and Prototype Planning
7 Material Selection in Engineering Design
8 Product Lifecycle and Sustainability
9 Design for Manufacture and Design for Additive Manufacture
Lecture notesavailable on Moodle
LiteratureUlrich and Eppinger, Product Design and Development, 6th Edition, McGraw Hill Education, 2016.

Cagan and Vogel, Creating Breakthrough Products: Revealing the Secrets that Drive Global Innovation, 2nd Edition, Pearson Education, 2013.
Prerequisites / NoticeAlthough the course is offered to ME (BSc and MSc) and CS (BSc and MSc) students, priority will be given to ME BSc students in the Focus Design, Mechanics, and Materials if the course is full.
151-0840-00LPrinciples of FEM-Based Optimization and Robustness Analysis Information W5 credits2V + 2UB. Berisha, P. Hora, N. Manopulo
AbstractThe course provides fundamentals of stochastic simulation and non-linear optimization methods. Methods of non-linear optimizaion for complex mechanical systems will be introduced und applied on real processes. Typical applications of stochastical methods for the prediction of process stability and robustness analysis will be discussed.
ObjectiveReal systems are, in general, of non-linear nature. Moreover, they are submitted to process parameter variations. In spite of this, most research is performed assuming deterministic boundary conditions, in which all parameters are constant. As a consequence, such research cannot draw conclusions on real system behavior, but only on behavior under singular conditions. Hence, the objective of this course is to give an insight into stochastic simulations and non-linear optimization methods.

Students will learn mathematical methods e.g. gradient based and gradient free methods like genetic algorithm, and optimization tools (Matlab Optimization Toolbox) to solve basic optimization and stochastic problems.

Furthermore, special attention will be paid to the modeling of engineering problems using a commercial finite element program e.g. LS-Dyna to evaluate the mechanical response of a system, and an optimization tool e.g. LS-Opt for the mathematical optimization and robustness analysis.
ContentPrinciples of nonlinear optimization

- Introduction into nonlinear optimization and stochastic process simulation
- Principles of nonlinear optimization
- Introduction into the design optimization and probabilistic tool LS-Opt
- Design of Experiments DoE
- Introduction into nonlinear finite element methods

Optimization of nonlinear systems

- Application: Optimization of simple structures using LS-Opt and LS-Dyna
- Optimization based on meta modeling techniques
- Introduction into structure optimization
- Introduction into geometry parameterization for shape and topology optimization

Robustness and sensitivity of multiparameter systems

- Introduction into stochastics and robustness of processes
- Sensitivity analysis
- Application examples
Lecture notesyes
151-0206-00LEnergy Systems and Power EngineeringW4 credits2V + 2UR. S. Abhari, A. Steinfeld
AbstractIntroductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.
ObjectiveIntroductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.
ContentWorld primary energy resources and use: fossil fuels, renewable energies, nuclear energy; present situation, trends, and future developments. Sustainable energy system and environmental impact of energy conversion and use: energy, economy and society. Electric power and the electricity economy worldwide and in Switzerland; production, consumption, alternatives. The electric power distribution system. Renewable energy and power: available techniques and their potential. Cost of electricity. Conventional power plants and their cycles; state-of-the -art and advanced cycles. Combined cycles and cogeneration; environmental benefits. Solar thermal power generation and solar photovoltaics. Hydrogen as energy carrier. Fuel cells: characteristics, fuel reforming and combined cycles. Nuclear power plant technology.
Lecture notesVorlesungsunterlagen werden verteilt
151-0306-00LVisualization, Simulation and Interaction - Virtual Reality I Information W4 credits4GA. Kunz
AbstractTechnology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).
ObjectiveThe product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process.
ContentIntroduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.
Lecture notesA complete version of the handout is also available in English.
Prerequisites / NoticeVoraussetzungen:
Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF

Testat/ Kredit-Bedingungen/ Prüfung:
– Teilnahme an Vorlesung und Kolloquien
– Erfolgreiche Durchführung von Übungen in Teams
– Mündliche Einzelprüfung 30 Minuten
151-0314-00LInformation Technologies in the Digital ProductW4 credits3GE. Zwicker, R. Montau
AbstractObjectives, Concepts and Methods of Digitalization, Digital Product and Product Lifecycle Management (PLM)
Digital Product Fundamentals: Product Structures, Optimization of Development- and Engineering Processes, Utilization of digital models in Sales, Production, Service for Industry 4.0 Strategies
PLM Fundamentals: Objects, Structures, Processes, Integrations, Visualization
Best Practices
ObjectiveStudents learn the basics and concepts of Digitalization along the product creation process on the foundation of Product Lifecycle Management (PLM) technologies, the usage of databases, the integration of CAx systems and Visualization, the configuration of computer-based collaboration leveraging standards and protocols as well as variant and configuration management to enable an efficient utilization of the digital product approach in industry 4.0.
ContentPossibilities and potential of modern IT applications with focus on CAx and PLM technologies for targeted utilization in the context of product platform - business processes - IT tools. Introduction to the concepts of Product Lifecycle Management (PLM): information modeling, data management, revision, usage and distribution of product data. Structure and functional principles of PLM systems. Integration of new IT technologies in business processes. Possibilities of publication and automatic configuration of product variants on the Internet. Using state-of-the-art information and communication technologies to develop products globally across distributed locations. Interfaces in computer-integrated product development. Selection, configuration, adaptation and introduction of PLM systems. Examples and case studies for industrial usage of modern information technologies.

Learning modules:
- Introduction to Digitalization (Digital Product, PLM technology)
- Database technology (foundation of digitalization)
- Object Management
- Object Classification
- Object identification with Part Numbering Systems
- CAx/PLM integration with Visualization/AR
- Workflow & Change Management
- Interfaces of the Digital Product
- Enterprise Application Integration (EAI)
Lecture notesDidactic concept / learning materials:
The course consists of lectures and exercises based on practical examples.
Provision of lecture handouts and script digitally in Moodle.
Prerequisites / NoticePrerequisites: None
Recommended: Fokus-Project, interest in Digitalization
Lecture appropriate for D-MAVT, D-MTEC and D-INF

Testat/ Credit Requirements / Exam:
- Successful execution of exercises in teams
- Oral exam 30 minutes, based on concrete problem cases
151-0660-00LModel Predictive Control Information W4 credits2V + 1UM. Zeilinger
AbstractModel predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
ObjectiveDesign and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Content- Review of required optimal control theory
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
Lecture notesScript / lecture notes will be provided.
Prerequisites / NoticeOne semester course on automatic control, Matlab, linear algebra.
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.

Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
151-0940-00LModelling and Mathematical Methods in Process and Chemical EngineeringW4 credits3GM. Mazzotti
AbstractStudy of the non-numerical solution of systems of ordinary differential equations and first order partial differential equations, with application to chemical kinetics, simple batch distillation, and chromatography.
ObjectiveStudy of the non-numerical solution of systems of ordinary differential equations and first order partial differential equations, with application to chemical kinetics, simple batch distillation, and chromatography.
ContentDevelopment of mathematical models in process and chemical engineering, particularly for chemical kinetics, batch distillation, and chromatography. Study of systems of ordinary differential equations (ODEs), their stability, and their qualitative analysis. Study of single first order partial differential equation (PDE) in space and time, using the method of characteristics. Application of the theory of ODEs to population dynamics, chemical kinetics (Belousov-Zhabotinsky reaction), and simple batch distillation (residue curve maps). Application of the method of characteristic to chromatography.
Lecture notesno skript
LiteratureA. Varma, M. Morbidelli, "Mathematical methods in chemical engineering," Oxford University Press (1997)
H.K. Rhee, R. Aris, N.R. Amundson, "First-order partial differential equations. Vol. 1," Dover Publications, New York (1986)
R. Aris, "Mathematical modeling: A chemical engineer’s perspective," Academic Press, San Diego (1999)
151-0980-00LBiofluiddynamicsW4 credits2V + 1UD. Obrist, P. Jenny
AbstractIntroduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics).
ObjectiveA basic understanding of fluid dynamical processes in the human body. Knowledge of the basic concepts of fluid dynamics and the ability to apply these concepts appropriately.
ContentThis lecture is an introduction to the fluid dynamics of the human body (biomedical fluid dynamics). For selected topics of human physiology, we introduce fundamental concepts of fluid dynamics (e.g., creeping flow, incompressible flow, flow in porous media, flow with particles, fluid-structure interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, blood rheology, microcirculation, respiratory fluid dynamics and fluid dynamics of the inner ear.
Lecture notesLecture notes are provided electronically.
LiteratureA list of books on selected topics of biofluiddynamics can be found on the course web page.
227-0052-10LElectromagnetic Fields and Waves Information W6 credits3V + 2UL. Novotny
AbstractThis course is focused on the generation and propagation of electromagnetic fields. Based on Maxwell's equations we will derive the wave equation and its solutions. Specifically, we will discuss fields and waves in free space, refraction and reflection at plane interfaces, dipole radiation and Green functions, vector and scalar potentials, as well as gauge transformations.
ObjectiveUnderstanding of electromagnetic fields
227-0418-00LAlgebra and Error Correcting Codes Information W6 credits4GH.‑A. Loeliger
AbstractThe course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
ObjectiveThe course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
ContentError correcting codes: coding and modulation, linear codes, Hamming space codes, Euclidean space codes, trellises and Viterbi decoding, convolutional codes, factor graphs and message passing algorithms, low-density parity check codes, turbo codes, polar codes, Reed-Solomon codes.

Algebra: groups, rings, homomorphisms, quotient groups, ideals, finite fields, vector spaces, polynomials.
Lecture notesLecture Notes (english)
227-0420-00LInformation Theory II Information W6 credits2V + 2UA. Lapidoth, S. M. Moser
AbstractThis course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
ObjectiveThe course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory.
ContentDifferential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem.
Lecture notesn/a
LiteratureT.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
227-0104-00LCommunication and Detection Theory Information W6 credits4GA. Lapidoth
AbstractThis course teaches the foundations of modern digital communications and detection theory. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise.
ObjectiveThis is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum.
Content- Baseband representation of passband signals.
- Bandwidth and inner products in baseband and passband.
- The geometry of the space of energy-limited signals.
- The Sampling Theorem as an orthonormal expansion.
- Sampling passband signals.
- Pulse Amplitude Modulation (PAM): energy, power, and power spectral density.
- Nyquist Pulses.
- Quadrature Amplitude Modulation (QAM).
- Hypothesis testing.
- The Bhattacharyya Bound.
- The multivariate Gaussian distribution
- Gaussian stochastic processes.
- Detection in white Gaussian noise.
Lecture notesn/a
LiteratureA. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017)
227-0120-00LCommunication Networks Information W6 credits4GL. Vanbever
AbstractAt the end of this course, you will understand the fundamental concepts behind communication networks and the Internet. Specifically, you will be able to:

- understand how the Internet works;
- build and operate Internet-like infrastructures;
- identify the right set of metrics to evaluate the performance of a network and propose ways to improve it.
ObjectiveAt the end of the course, the students will understand the fundamental concepts of communication networks and Internet-based communications. Specifically, students will be able to:

- understand how the Internet works;
- build and operate Internet-like network infrastructures;
- identify the right set of metrics to evaluate the performance or the adequacy of a network and propose ways to improve it (if any).

The course will introduce the relevant mechanisms used in today's networks both from an abstract perspective but also from a practical one by presenting many real-world examples and through multiple hands-on projects.

For more information about the lecture, please visit:
Lecture notesLecture notes and material for the course will be available before each course on:
LiteratureMost of course follows the textbook "Computer Networking: A Top-Down Approach (6th Edition)" by Kurose and Ross.
Prerequisites / NoticeNo prior networking background is needed. The course will include some programming assignments (in Python) for which the material covered in Technische Informatik 1 (227-0013-00L) and Technische Informatik 2 (227-0014-00L) will be useful.
227-0158-00LSemiconductor Devices: Transport Theory and Monte Carlo Simulation
Does not take place this semester.
W4 credits2V + 1U
AbstractThe first part deals with semiconductor transport theory including the necessary quantum mechanics.
In the second part, the Boltzmann equation is solved with the stochastic methods of Monte Carlo simulation.
The exercises address also TCAD simulations of MOSFETs. Thus the topics include theoretical physics,
numerics and practical applications.
ObjectiveOn the one hand, the link between microscopic physics and its concrete application in device simulation is established; on the other hand, emphasis is also laid on the presentation of the numerical techniques involved.
ContentQuantum theoretical foundations I (state vectors, Schroedinger and Heisenberg picture). Band structure (Bloch theorem, one dimensional periodic potential, density of states). Pseudopotential theory (crystal symmetries, reciprocal lattice, Brillouin zone).
Semiclassical transport theory (Boltzmann transport equation (BTE), scattering processes, linear transport).<br>
Monte Carlo method (Monte Carlo simulation as solution method of the BTE, algorithm, expectation values).<br>
Implementational aspects of the Monte Carlo algorithm (discretization of the Brillouin zone, self-scattering according to Rees, acceptance- rejection method etc.). Bulk Monte Carlo simulation (velocity-field characteristics, particle generation, energy distributions, transport parameters). Monte Carlo device simulation (ohmic boundary conditions, MOSFET simulation).
Quantum theoretical foundations II (limits of semiclassical transport theory, quantum mechanical derivation of the BTE, Markov-Limes).
Lecture notesLecture notes (in German)
227-0159-00LSemiconductor Devices: Quantum Transport at the Nanoscale Information W6 credits2V + 2UM. Luisier, A. Emboras, J. Godet
AbstractThis class offers an introduction into quantum transport theory, a rigorous approach to electron transport at the nanoscale. It covers different topics such as bandstructure, Wave Function and Non-equilibrium Green's Function formalisms, and electron interactions with their environment. Matlab exercises accompany the lectures where students learn how to develop their own transport simulator.
ObjectiveThe continuous scaling of electronic devices has given rise to structures whose dimensions do not exceed a few atomic layers. At this size, electrons do not behave as particle any more, but as propagating waves and the classical representation of electron transport as the sum of drift-diffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on state-of-the-art quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator.
ContentThe following topics will be addressed:
- Introduction to quantum transport modeling
- Bandstructure representation and effective mass approximation
- Open vs closed boundary conditions to the Schrödinger equation
- Comparison of the Wave Function and Non-equilibrium Green's Function formalisms as solution to the Schrödinger equation
- Self-consistent Schödinger-Poisson simulations
- Quantum transport simulations of resonant tunneling diodes and quantum well nano-transistors
- Top-of-the-barrier simulation approach to nano-transistor
- Electron interactions with their environment (phonon, roughness, impurity,...)
- Multi-band transport models
Lecture notesLecture slides are distributed every week and can be found at
LiteratureRecommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997
Prerequisites / NoticeBasic knowledge of semiconductor device physics and quantum mechanics
227-0558-00LPrinciples of Distributed Computing Information W6 credits2V + 2U + 1AR. Wattenhofer, M. Ghaffari
AbstractWe study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.
ObjectiveDistributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week.
ContentDistributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems.

Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds
Lecture notesAvailable. Our course script is used at dozens of other universities around the world.
LiteratureLecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world.

Distributed Computing: Fundamentals, Simulations and Advanced Topics
Hagit Attiya, Jennifer Welch.
McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6

Introduction to Algorithms
Thomas Cormen, Charles Leiserson, Ronald Rivest.
The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8

Disseminatin of Information in Communication Networks
Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger.
Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2

Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes
Frank Thomson Leighton.
Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1

Distributed Computing: A Locality-Sensitive Approach
David Peleg.
Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8
Prerequisites / NoticeCourse pre-requisites: Interest in algorithmic problems. (No particular course needed.)
252-0211-00LInformation Security Information W8 credits4V + 3UD. Basin, S. Capkun, E. Mohammadi
AbstractThis course provides an introduction to Information Security. The focus
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
ObjectiveMaster fundamental concepts in Information Security and their
application to system building. (See objectives listed below for more details).
Content1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
252-0407-00LCryptography Foundations Information
Takes place the last time in this form.
W7 credits3V + 2U + 1AU. Maurer
AbstractFundamentals and applications of cryptography. Cryptography as a mathematical discipline: reductions, constructive cryptography paradigm, security proofs. The discussed primitives include cryptographic functions, pseudo-randomness, symmetric encryption and authentication, public-key encryption, key agreement, and digital signature schemes. Selected cryptanalytic techniques.
ObjectiveThe goals are:
(1) understand the basic theoretical concepts and scientific thinking in cryptography;
(2) understand and apply some core cryptographic techniques and security proof methods;
(3) be prepared and motivated to access the scientific literature and attend specialized courses in cryptography.
ContentSee course description.
Lecture notesyes.
Prerequisites / NoticeFamiliarity with the basic cryptographic concepts as treated for
example in the course "Information Security" is required but can
in principle also be acquired in parallel to attending the course.
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