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pro vyhledávání: '"Pendlebury, Feargus"'
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
Autor:
Arp, Daniel, Quiring, Erwin, Pendlebury, Feargus, Warnecke, Alexander, Pierazzi, Fabio, Wressnegger, Christian, Cavallaro, Lorenzo, Rieck, Konrad
Publikováno v:
Communications of the ACM; Nov2024, Vol. 67 Issue 11, p104-112, 9p
Autor:
Yang, Limin, Chen, Zhi, Cortellazzi, Jacopo, Pendlebury, Feargus, Tu, Kevin, Pierazzi, Fabio, Cavallaro, Lorenzo, Wang, Gang
Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibility of backdoor attacks against malware classifiers, and yet the stealt
Externí odkaz:
http://arxiv.org/abs/2202.05470
Autor:
Labaca-Castro, Raphael, Muñoz-González, Luis, Pendlebury, Feargus, Rodosek, Gabi Dreo, Pierazzi, Fabio, Cavallaro, Lorenzo
Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), which identify noisy patterns that generalize across the input space, allo
Externí odkaz:
http://arxiv.org/abs/2102.06747
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
Machine learning for malware classification shows encouraging results, but real deployments suffer from performance degradation as malware authors adapt their techniques to evade detection. This phenomenon, known as concept drift, occurs as new malwa
Externí odkaz:
http://arxiv.org/abs/2010.03856
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
Autor:
Pendlebury, Feargus, Pierazzi, Fabio, Jordaney, Roberto, Kinder, Johannes, Cavallaro, Lorenzo
Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are commonly inflated due to two pervasive sources of experimental bias: "spa
Externí odkaz:
http://arxiv.org/abs/1807.07838
Akademický článek
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Autor:
Cavallaro, Lorenzo, Kinder, Johannes, Pendlebury, Feargus, Pierazzi, Fabio, Massacci, Fabio, Bodden, Eric, Sabetta, Antonino
Publikováno v:
IEEE Security and Privacy, 21(2), 53-56. Institute of Electrical and Electronics Engineers Inc.
Cavallaro, L, Kinder, J, Pendlebury, F, Pierazzi, F, Massacci, F, Bodden, E & Sabetta, A 2023, ' Are Machine Learning Models for Malware Detection Ready for Prime Time? ', IEEE Security and Privacy, vol. 21, no. 2, pp. 53-56 . https://doi.org/10.1109/MSEC.2023.3236543
Cavallaro, L, Kinder, J, Pendlebury, F, Pierazzi, F, Massacci, F, Bodden, E & Sabetta, A 2023, ' Are Machine Learning Models for Malware Detection Ready for Prime Time? ', IEEE Security and Privacy, vol. 21, no. 2, pp. 53-56 . https://doi.org/10.1109/MSEC.2023.3236543
We investigate why the performance of machine learning models for malware detection observed in a lab setting often cannot be reproduced in practice. We discuss how to set up experiments mimicking a practical deployment and how to measure the robustn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e3c6b266c67929678b377be7d1f3080
https://research.vu.nl/en/publications/021c7e33-4ff2-44a1-a1c1-ccb0fe339b13
https://research.vu.nl/en/publications/021c7e33-4ff2-44a1-a1c1-ccb0fe339b13