no code implementations • 2 Feb 2024 • Zeliang Kan, Shae McFadden, Daniel Arp, Feargus Pendlebury, Roberto Jordaney, Johannes Kinder, Fabio Pierazzi, Lorenzo Cavallaro
Machine learning (ML) plays a pivotal role in detecting malicious software.
1 code implementation • 25 May 2022 • Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring
In particular, we show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image.
1 code implementation • 19 Oct 2020 • Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck
Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware.
no code implementations • 19 Oct 2020 • Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas.
2 code implementations • 5 Jun 2019 • Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck
Deep learning is increasingly used as a building block of security systems.
no code implementations • 28 Apr 2017 • Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection.
Cryptography and Security
no code implementations • 16 Mar 2017 • Erwin Quiring, Daniel Arp, Konrad Rieck
This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods.