Anzeige
Erschienen in:
2023 | OriginalPaper | Buchkapitel
10. Comparison of Strategies
verfasst von : Raphael Labaca-Castro
Erschienen in: Machine Learning under Malware Attack
Verlag: Springer Fachmedien Wiesbaden
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Having properly introduced each module of FAME, we now proceed to evaluate the problem-space attack suite with a holistic approach in order to better understand the advantages and disadvantages of each strategy. As depicted in Fig. 1.1, our goal is to create an initial benchmark to evaluate adversarial examples in the context of malware using PE files. Notably, since gradient-based approaches (i.e., GRIPE in Chapter 8) and GAN attacks (i.e., GAINED in Chapter 9) generate feature-space adversarial examples, we did not include them in the main suite of realizable attacks implemented in FAME.