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Data Mining for Cyber Security


Admission requirements

Admission only after intake, please see our website


The course will provide an overview of data mining and data stream mining techniques and their applications to cyber security. The course teaches you how to go from raw input data to useful results in credit-card fraud detection, real-time detection of attacks and deviations in SCADA systems, and detection of botnet activities in network traffic. Practical work with real data will provide the participants some hands-on experience with applying data mining to real-life problems. Limitations of data mining in cyber security will also be discussed, in particular the ability of adversaries to modify their data and how to deal with this. Privacy issues and solutions to increase the privacy of individuals in a data set will be reviewed.

Course objectives

Participants will get:

  • Knowledge and understanding of the potentials and pitfalls of using data mining in cyber security

  • knowledge and understanding of the inner workings of data mining solutions for cyber data * mining data where the majority of data are benign * detecting anomalies * mining sequential data * learning profiles and fingerprints

  • the ability to correctly apply data mining tools on real-world problems

  • knowledge and understanding of privacy and adversarial aware data mining

Prerequisite knowledge is a basic understanding of statistics. Experience with machine learning or data mining algorithms is useful but not required.


4 days from 9.30 until 17.00
Friday June 18, 2021
Friday June 25, 2021
Friday July 2, 2021 – deadline Lab 1
Friday July 9, 2021 – deadline Lab 2
Friday July 16, 2021 no lecture but– deadline Lab 3

Mode of instruction

Lectures, exercises, class discussion
Lecturers: Dr. Sicco Verwer (TUD)

Assessment method

Lab 1

  • 40% of final grade

  • Grade must be 5.50 or higher to pass the course

  • Resit of a fail is possible.

  • Resit will take the same form

Lab 2

  • 30% of final grade

  • Grade must be compensated

  • Resit not possible

Lab 3

  • 30% of final grade

  • Grade must be compensated

  • Resit not possible

Only assessments with the weight of 30% and lower are compensable. This means that one does not have to pass an assessment if it weighs 30% or less in order to pass the course, if the average of all assessments combined is at least a 5.5. In addition, assignments weighing up to and including 30% are not re-sitable, meaning that if one failed an assessment of 30% or less one is not allowed to redo it and that assessment must be compensated by the other assessment(s).

Reading list

Compulsory literature and literature for further consultation will be announced via Brightspace


No registration is required for lectures and exams.


Dr. Sicco Verwer Chantal de Groot, study coordinator


For more information see the website see our website