Exercise Cybersecurity Data Science (PBL) |
Subtitle: |
This course is part of the module: Cybersecurity Data Science |
Semester: |
SoSe 24 |
Course type: |
PBL -Projekt-/problembasierte Lehrveranstaltung (Lehre) |
Course number: |
lv2915_s24 |
Lecturer: |
Riccardo Scandariato, Ina Weigl, Dr. Nicolás Díaz Ferreyra, Catherine Tony, Quang Cuong Bui, Torge Hinrichs, Emanuele Iannone |
Description: |
Theoretical Foundations: - Introduction to data science
- Supervised and unsupervised learning
- Data science methods (e.g., clustering, decision trees, artificial neural networks)
- Performance metrics
Cybersecutrity Applications: - Spam detection
- Phishing detection
- Intrusion detection
- Access-control prediction
- Denial of Service (DoS) prediction
- Vulnerability/malware prediction
- Adversarial machine learning
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Performance accreditation: |
m1773-2022 - Cybersecurity Data Science<ul><li>p1760-2022 - Cybersecurity Data Science: Klausur schriftlich</li></ul><br>m1773-2023 - Cybersecurity Data Science<ul><li>p1760-2022 - Cybersecurity Data Science: Klausur schriftlich</li><li>vl440-2023 - Voluntary Course Work Cybersecurity Data Science - Subject theoretical and practical work: Subject theoretical and practical work</li></ul> |
ECTS credit points: |
3 |
Stud.IP informationen about this course: |
Home institute: Institut für Software Security (E-22)
Registered participants in Stud.IP: 127
Documents: 4
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