889 citations · 1.7k across the 10 of their papers we have counts for
7 papers · 1 filter
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
Moshe Kravchik, Battista Biggio, Asaf Shabtai
Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detecto…
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio +2
Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that…
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Davide Maiorca, Battista Biggio, Giorgio Giacinto
Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedde…
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…
Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca +5
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack s…
Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware
Davide Maiorca, Battista Biggio
Over the last decade, malicious software (or malware, for short) has shown an increasing sophistication and proliferation, fueled by a flourishing underground economy, in response…