12 citations · 28 across the 5 of their papers we have counts for
5 papers
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Biagio Montaruli, Luca Demetrio, Maura Pintor +3
Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks recen…
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks
Yang Zheng, Luca Demetrio, Antonio Emanuele Cinà +6
RGB-D object recognition systems improve their predictive performances by fusing color and depth information, outperforming neural network architectures that rely solely on colors.…
Explaining Machine Learning DGA Detectors from DNS Traffic Data
Giorgio Piras, Maura Pintor, Luca Demetrio +1
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of…
Robust Machine Learning for Malware Detection over Time
Daniele Angioni, Luca Demetrio, Maura Pintor +1
The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more e…
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Fabio Roli
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this…