General
- study semester
-
5-6
- standard study semester
-
6
- cycle
- occasional
- duration
- 1 semester
- SWS
- 4
- ECTS
- 6
- teaching language
- English
People
- responsible
-
Prof. Dr. Mario Fritz
- lectures
-
Prof. Dr. Mario Fritz
Assessment & Grades
- entrance requirements
-
Data Science/Statistics Course
- assessment / exams
Übungen, Projekt und mündliche Prüfung
- grade
Das Modul ist insgesamt bestanden, wenn die Prüfungsleistungen bestanden wurden.
Workload
- course type /weekly hours
2 h lectures
+ 2 h tutorial
= 4 h (weekly)
- total workload
60 h of classes
+ 120 h private study
= 180 h (= 6 ECTS)
Aims / Competences to be developed
Students know about the opportunities and risks of applying machine learning in cyber security. They understand a range of attacks and defense strategies and are capable of implementing such techniques. Students are aware of privacy risks of machine learning methods and understand how such risks can be mitigated.
Content
- Machine learning methodology in the context of cyber security
- Applications and opportunities of learning in cyber security
- Risks and attacks on machine learning in cyber security
- Malware classification
- Anomaly detection
- Intrusion detection
- Evasion attacks
- Model stealing
- Privacy risks and attacks
- Privacy protection
Literature & Reading
The teaching material will be in English and it will be announced at the beginning of the lecture.
Additional Information
Curriculum
This module is part of the following study programmes:
Cybersicherheit BSc: Vertiefungsvorlesungen der Cybersicherheit
study semester: 5-6 / standard study semester: 6
Cybersecurity MSc: Vertiefungsvorlesungen Cybersecurity
study semester: 1-3 / standard study semester: 4
Cybersecurity BSc (English): Kernthemen der Cybersicherheit
study semester: 5-6 / standard study semester: 6