Zobrazeno 1 - 10
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pro vyhledávání: '"Arp, Daniel"'
Autor:
Arp, Daniel1,2 (AUTHOR) d.arp@tu-berlin.de, Quiring, Erwin3,4 (AUTHOR) erwin.quiring@rub.de, Pendlebury, Feargus5 (AUTHOR) feargus@trustypatch.es, Warnecke, Alexander1,2 (AUTHOR) a.warnecke@tu-berlin.de, Pierazzi, Fabio6 (AUTHOR) fabio.pierazzi@kcl.ac.uk, Wressnegger, Christian7,8 (AUTHOR) c.wressnegger@kit.edu, Cavallaro, Lorenzo5 (AUTHOR) l.cavallaro@ucl.ac.uk, Rieck, Konrad1,2 (AUTHOR) rieck@tu-berlin.de
Publikováno v:
Communications of the ACM. Nov2024, Vol. 67 Issue 11, p104-112. 9p.
Autor:
Kan, Zeliang, McFadden, Shae, Arp, Daniel, Pendlebury, Feargus, Jordaney, Roberto, Kinder, Johannes, Pierazzi, Fabio, Cavallaro, Lorenzo
Machine learning (ML) plays a pivotal role in detecting malicious software. Despite the high F1-scores reported in numerous studies reaching upwards of 0.99, the issue is not completely solved. Malware detectors often experience performance decay due
Externí odkaz:
http://arxiv.org/abs/2402.01359
Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts fro
Externí odkaz:
http://arxiv.org/abs/2205.12543
Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware. In this paper, we outline our learning-b
Externí odkaz:
http://arxiv.org/abs/2010.09569
Autor:
Arp, Daniel, Quiring, Erwin, Pendlebury, Feargus, Warnecke, Alexander, Pierazzi, Fabio, Wressnegger, Christian, Cavallaro, Lorenzo, Rieck, Konrad
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. This development has influenced computer security, spawni
Externí odkaz:
http://arxiv.org/abs/2010.09470
Autor:
Cortellazzi, Jacopo, Pendlebury, Feargus, Arp, Daniel, Quiring, Erwin, Pierazzi, Fabio, Cavallaro, Lorenzo
Recent research efforts on adversarial machine learning (ML) have investigated problem-space attacks, focusing on the generation of real evasive objects in domains where, unlike images, there is no clear inverse mapping to the feature space (e.g., so
Externí odkaz:
http://arxiv.org/abs/1911.02142
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developin
Externí odkaz:
http://arxiv.org/abs/1906.02108
Autor:
Demontis, Ambra, Melis, Marco, Biggio, Battista, Maiorca, Davide, Arp, Daniel, Rieck, Konrad, Corona, Igino, Giacinto, Giorgio, Roli, Fabio
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. However, its security against well-crafted attacks has not only been recently
Externí odkaz:
http://arxiv.org/abs/1704.08996
Machine learning is increasingly used in security-critical applications, such as autonomous driving, face recognition and malware detection. Most learning methods, however, have not been designed with security in mind and thus are vulnerable to diffe
Externí odkaz:
http://arxiv.org/abs/1703.05561
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