Symbol Kurs

Practical Course: Intelligent Data Analysis for Computer Security (Datalab)

n this practical course, the students develop learning-based systems for different computer security tasks, thereby intensifying their knowledge gained in the lecture "Machine Learning for Computer Security." The students have the unique opportunity to design, implement, and evaluate systems based on real-world data used in computer security research. The "Datalab" is composed of 6 units with several individual tasks covering different topics from classical computer security research, such as attack detection, spam classification, or vulnerability discovery. In each unit, the students develop an approach, train and validate it on known data, and submit their solution to the course platform, where the approach is tested against unknown data. The best approaches are awarded at the end of the semester and presented at a joint colloquium and get-together. More information can be found at https://intellisec.de/teaching/datalab
Status: Offline

Zusammenfassung

n this practical course, the students develop learning-based systems for different computer security tasks, thereby intensifying their knowledge gained in the lecture "Machine Learning for Computer Security." The students have the unique opportunity to design, implement, and evaluate systems based on real-world data used in computer security research. The "Datalab" is composed of 6 units with several individual tasks covering different topics from classical computer security research, such as attack detection, spam classification, or vulnerability discovery. In each unit, the students develop an approach, train and validate it on known data, and submit their solution to the course platform, where the approach is tested against unknown data. The best approaches are awarded at the end of the semester and presented at a joint colloquium and get-together. More information can be found at https://intellisec.de/teaching/datalab

Allgemein

Sprache
Deutsch

Verfügbarkeit

Zugriff
01. Sep 2021, 11:55 - 01. Mär 2022, 11:55
Aufnahmeverfahren
Sie können diesem Kurs direkt beitreten.
Zeitraum für Beitritte
Bis: 31. Okt 2021, 15:40
Veranstaltungszeitraum
26. Okt 2021 - 15. Feb 2022

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2147173
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Erstellt am
02. Aug 2021, 13:50