8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CR2023
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…
cs.CR2023★ 1 cited
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…
cs.CR2021★ 8 cited
Longitudinal Study of the Prevalence of Malware Evasive Techniques
Lorenzo Maffia, Dario Nisi, Platon Kotzias +3
By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of…