Suchergebnis: Katalogdaten im Herbstsemester 2017

Informatik Master Information
Vertiefungsfächer
Vertiefung in Information Systems
Kernfächer der Vertiefung in Information Systems
NummerTitelTypECTSUmfangDozierende
252-0463-00LSecurity Engineering Information W5 KP2V + 2UD. Basin
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
252-0535-00LMachine Learning Information W8 KP3V + 2U + 2AJ. M. Buhmann
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 the most important 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. A machine learning project 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:

- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
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 at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution.
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