3 citations · 6 across the 6 of their papers we have counts for
6 papers
The Role of Domain-Specific Features in Malware Detection: A macOS Case Study
Biagio Montaruli, Andrea Oliveri, Savino Dambra +1
Despite the growing popularity of macOS among end users and enterprise systems, malware research has primarily focused on Windows and Android operating systems, leaving the problem…
"Do Users fall for Real Adversarial Phishing?" Investigating the Human response to Evasive Webpages
Ajka Draganovic, Savino Dambra, Javier Aldana Iuit +2
Phishing websites are everywhere, and countermeasures based on static blocklists cannot cope with such a threat. To address this problem, state-of-the-art solutions entail the appl…
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
Savino Dambra, Yufei Han, Simone Aonzo +5
Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in di…
Quantifying Carbon Emissions due to Online Third-Party Tracking
Michalis Pachilakis, Savino Dambra, Iskander Sanchez-Rola +1
In the past decade, global warming made several headlines and turned the attention of the whole world to it. Carbon footprint is the main factor that drives greenhouse emissions up…
One Size Does not Fit All: Quantifying the Risk of Malicious App Encounters for Different Android User Profiles
Savino Dambra, Leyla Bilge, Platon Kotzias +2
Previous work has investigated the particularities of security practices within specific user communities defined based on country of origin, age, prior tech abuse, and economic st…
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra +3
Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learn…