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EL2850 Cyber-Physical Security in Time-Critical Systems 7.5 credits

The course covers theory and methods for security in cyber-physical systems. In particular, time-critical systems in critical infrastructures and autonomous systems, where cyber-attacks and faults may have physical consequences, are studied.

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Headings with content from the Course syllabus EL2850 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course covers safety aspects in cyber-physical systems. Particularly, time-critical systems in critical infrastructure and autonomous systems are studied, where cyberattacks and errors can have physical consequences. A large part of the course is devoted to presentation by basic principles and methods for modelling, analysis and detection of errors and cyberattacks in dynamic system. In particular, the following is studied

  • Documented attacks against cyber-physical systems, system architectures, safety and accessibility, risk management and attack-space in cyber-physical systems.
  • Model-based quantification of physical consequences of errors and cyberattacks, discrete-time dynamic systems (linear state models), observers, strong observability and detectability.
  • Model and data-based error detection, fault identification and redundancy, parity space methods, observer based methods, setting of threshold.
  • Statistical anomaly detection, hypothesis testing, Neyman-Pearson's lemma, generalised likelihood ratio (GLR), Bayes' theorem, principal component analysis (PCA), detection of abrupt process changes, cumulative sum test (CUSUM), machine-learning based methods.

Intended learning outcomes

After passing the course, the student shall be able to

  • formulate basic theory and definitions of important concepts in safety in cyber-physical systems in general and time-critical systems in particular
  • apply model and data-based methods for safety in cyber-physical systems particularly for time-critical systems.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

Knowledge in algebra and geometry, 7.5 higher education credits, equivalent to completed course SF1624.

Knowledge in multivariable analysis, 7.5 higher education credits, equivalent to completed course SF1626.

Knowledge in probability theory and statistics, 6 higher education credits, equivalent completed course SF1900/SF1912/SF1918/SF1922/SF1924.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • INL1 - Assignment, 2.5 credits, grading scale: P, F
  • INL2 - Assignment, 2.5 credits, grading scale: P, F
  • TEN1 - Written exam, 2.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web EL2850

Offered by

Main field of study

Computer Science and Engineering, Electrical Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

In this course, the EECS code of honor applies, see: