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Maschinelles Lernen für die Computersicherheit

The lecture is about combining the fields of machine learning and computer security in practice. Many tasks in the computer security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more "intelligent" security solutions. However, also systems based on machine learning can be attacked and need to be secured. The module introduces students to theoretic and practical aspects of machine learning in computer security. We cover basics on features, feature engineering, and feature spaces in the security domain, discuss the application of clustering and anomaly detection for malware analysis and intrusion detection, as well as, the discovery of vulnerabilities using machine learning. Additionally, we discuss the interpretability and robustness of learning-based systems.
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Zusammenfassung

The lecture is about combining the fields of machine learning and computer security in practice. Many tasks in the computer security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more "intelligent" security solutions. However, also systems based on machine learning can be attacked and need to be secured.

The module introduces students to theoretic and practical aspects of machine learning in computer security. We cover basics on features, feature engineering, and feature spaces in the security domain, discuss the application of clustering and anomaly detection for malware analysis and intrusion detection, as well as, the discovery of vulnerabilities using machine learning. Additionally, we discuss the interpretability and robustness of learning-based systems.

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Sprache
Englisch
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This work has all rights reserved by the owner.

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Zugriff
01. Mär 2020, 12:00 - 30. Okt 2020, 12:00
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Sie können diesem Kurs direkt beitreten.
Zeitraum für Beitritte
Bis: 31. Jul 2020, 12:00
Spätester Kursaustritt
31. Jul 2020
Veranstaltungszeitraum
22. Apr 2020 - 31. Jul 2020

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1528768
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Erstellt am
13. Mär 2020, 12:48