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.
no code implementations • 11 Feb 2022 • Limin Yang, Zhi Chen, Jacopo Cortellazzi, Feargus Pendlebury, Kevin Tu, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang
Empirically, we show that existing backdoor attacks in malware classifiers are still detectable by recent defenses such as MNTD.
no code implementations • 12 Feb 2021 • Raphael Labaca-Castro, Luis Muñoz-González, Feargus Pendlebury, Gabi Dreo Rodosek, Fabio Pierazzi, Lorenzo Cavallaro
Universal Adversarial Perturbations (UAPs), which identify noisy patterns that generalize across the input space, allow the attacker to greatly scale up the generation of such examples.
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.
no code implementations • 5 Nov 2019 • Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, Lorenzo Cavallaro
Second, building on our formalization, we propose a novel problem-space attack on Android malware that overcomes past limitations.
no code implementations • 20 Jul 2018 • Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, Lorenzo Cavallaro
Is Android malware classification a solved problem?