148 citations · 439 across the 31 of their papers we have counts for
6 papers · 1 filter
Super-sparse Learning in Similarity Spaces
Ambra Demontis, Marco Melis, Battista Biggio +2
In several applications, input samples are more naturally represented in terms of similarities between each other, rather than in terms of feature vectors. In these settings, machi…
Adversarial Detection of Flash Malware: Limitations and Open Issues
Davide Maiorca, Ambra Demontis, Battista Biggio +2
During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wi…
On Security and Sparsity of Linear Classifiers for Adversarial Settings
Ambra Demontis, Paolo Russu, Battista Biggio +2
Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to a…
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis +4
A number of online services nowadays rely upon machine learning to extract valuable information from data collected in the wild. This exposes learning algorithms to the threat of d…
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Marco Melis, Ambra Demontis, Battista Biggio +3
Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled…
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
Ambra Demontis, Marco Melis, Battista Biggio +6
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,…