Enterprise and IT Security (ENITS)
Modul manual
Data Mining
| Prerequisite |
Recommended knowledge and courses:
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| Teaching methods | Lecture/Seminar | ||||||
| Learning target / Competences |
LO1 Apply data mining techniques to detect and analyze cyber threats Students will be able to use classification, clustering, anomaly detection, and association analysis to identify suspicious patterns in network traffic, user behavior, and system logs, enabling early detection of cyber risks. LO2 Evaluate cyber risk exposure using data-driven models Students will be able to build and interpret predictive models (e.g., risk scoring, threat likelihood estimation) to assess vulnerabilities and quantify organizational cyber risk based on historical incident data and threat intelligence sources. LO3 Design data mining–supported mitigation strategies for cyber risk Students will be able to translate analytical findings into actionable cyber risk controls, recommending monitoring rules, automated alerts, and strategic mitigation measures grounded in mined data patterns. |
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| Duration | 1 | ||||||
| Hours per week | 4.0 | ||||||
| Overview |
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| ECTS | 6.0 | ||||||
| Requirements for awarding credit points |
Lab+Exam(K60) |
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| Credits and grades |
Lab+Exam(K60) |
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| Responsible person |
Prof. Dr. Dirk Drechsler |
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| Recommended semester | 2 | ||||||
| Frequency | Every 2nd sem. | ||||||
| Usability |
Master's Degree Program ENITS |