Admission requirements
Admission only after intake, see https://www.csacademy.nl/en/education/master-s-programmes/executive-master-s-programme-cyber-security
Elective in Master’s Programme Cyber Security
Description
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 have:
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
real-time data mining from vast data streams
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
Timetable
4 days from 9.30 until 17.00
Friday June 12, 2020
Friday June 19, 2020
Friday June 26, 2020
Friday July 3, 2020
Friday July 10, 2020 exam date
Mode of instruction
Lectures, seminars, exercises, class discussion, lab exercises
Lecturers: Dr. Sicco Verwer (TUD)
Assessment method
Assignment (50%), presentation (10%), written exam (40%)
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).
The resit will take the same form.
Reading list
Compulsory literature and literature for further consultation will be announced via Blackboard
Registration
No registration is required for lectures and exams.
Contact
Chantal de Groot programme manager