Symbol Kurs

Security of Machine Learning

This lecture explicitly focuses on the security of machine learning algorithms. In learning-based systems, often only average-case performances are considered to show the effectiveness of AI methods. Worse-case scenarios triggered by viciously crafted inputs, however, can be exploited by an adversary to cause devastating damage in the application area. It thus is of utmost importance to investigate, research, and know about the security properties of machine learning methods. The module introduces students to theoretic and practical aspects of security of machine learning algorithms and methods. In the first part, we cover offensive aspects of the topic. We will learn about different attack types such as adversarial examples (both white-box and black-box) or data poisoning and explicitly address problem-space constraints. In the second part, we explicitly focus on defensive mechanisms, such as adversarial training and network pruning. Finally, we will also cover methods for explaining learning-based algorithms to assist analysis and securing of machine learning methods. More information can be found at https://intellisec.de/teaching/secml
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Zusammenfassung

This lecture explicitly focuses on the security of machine learning algorithms. In learning-based systems, often only average-case performances are considered to show the effectiveness of AI methods. Worse-case scenarios triggered by viciously crafted inputs, however, can be exploited by an adversary to cause devastating damage in the application area. It thus is of utmost importance to investigate, research, and know about the security properties of machine learning methods.

The module introduces students to theoretic and practical aspects of security of machine learning algorithms and methods. In the first part, we cover offensive aspects of the topic. We will learn about different attack types such as adversarial examples (both white-box and black-box) or data poisoning and explicitly address problem-space constraints. In the second part, we explicitly focus on defensive mechanisms, such as adversarial training and network pruning. Finally, we will also cover methods for explaining learning-based algorithms to assist analysis and securing of machine learning methods. More information can be found at https://intellisec.de/teaching/secml

Allgemein

Sprache
Deutsch

Verfügbarkeit

Zugriff
01. Sep 2021, 00:00 - 01. Apr 2022, 00:00
Aufnahmeverfahren
Sie können diesem Kurs direkt beitreten.
Zeitraum für Beitritte
Bis: 15. Mär 2022, 11:55
Spätester Kursaustritt
01. Mär 2022
Veranstaltungszeitraum
20. Okt 2021 - 16. Feb 2022

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2147177
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Erstellt am
02. Aug 2021, 13:51