| 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
|
| 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: 122
Documents: 3
|
