1.4k citations · 4.6k across the 52 of their papers we have counts for
8 papers · 1 filter
Poisoning Behavioral Malware Clustering
Battista Biggio, Konrad Rieck, Davide Ariu +4
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for anti…
Is Data Clustering in Adversarial Settings Secure?
Battista Biggio, Ignazio Pillai, Samuel Rota Bulò +3
Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with d…
Face Presentation Attack Detection in Learned Color-liked Space
Lei Li, Zhaoqiang Xia, Xiaoyue Jiang +2
Face presentation attack detection (PAD) has become a thorny problem for biometric systems and numerous countermeasures have been proposed to address it. However, majority of them…
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor +5
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transfera…
Towards Adversarial Configurations for Software Product Lines
Paul Temple, Mathieu Acher, Battista Biggio +2
Ensuring that all supposedly valid configurations of a software product line (SPL) lead to well-formed and acceptable products is challenging since it is most of the time impractic…
Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown +3
Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation o…
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio +4
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fie…
Explaining Black-box Android Malware Detection
Marco Melis, Davide Maiorca, Battista Biggio +2
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on fea…