Search Results for author: Fabio Pierazzi

Found 10 papers, 0 papers with code

How to Train your Antivirus: RL-based Hardening through the Problem-Space

no code implementations29 Feb 2024 Jacopo Cortellazzi, Ilias Tsingenopoulos, Branislav Bošanský, Simone Aonzo, Davy Preuveneers, Wouter Joosen, Fabio Pierazzi, Lorenzo Cavallaro

It also makes possible to provide theoretical guarantees on the robustness of the model against a particular set of adversarial capabilities.

Malware Detection

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers

no code implementations11 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.

Backdoor Attack

Realizable Universal Adversarial Perturbations for Malware

no code implementations12 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.

Malware Classification

Dos and Don'ts of Machine Learning in Computer Security

no code implementations19 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.

BIG-bench Machine Learning Computer Security +1

Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]

no code implementations5 Nov 2019 Jacopo Cortellazzi, Feargus Pendlebury, Daniel Arp, Erwin Quiring, Fabio Pierazzi, Lorenzo Cavallaro

Secondly, building on our general formalization, we propose a novel problem-space attack on Android malware that overcomes past limitations in terms of semantics and artifacts.

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