8 citations · 8 across the 6 of their papers we have counts for
6 papers
DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors
Christian Scano, Diego Soi, Angelo Sotgiu +5
Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that, despite claims, existing prob…
Don't Trust Us: A privacy-by-design android malware detection pipeline
Emmanuele Massidda, Diego Soi, Giorgio Giacinto
Android malware detection increasingly relies on collecting and processing sensitive user data, including device identifiers, network artifacts, and runtime traces, while privacy i…
AndroWasm: an Empirical Study on Android Malware Obfuscation through WebAssembly
Diego Soi, Silvia Lucia Sanna, Lorenzo Pisu +2
In recent years, stealthy Android malware has increasingly adopted sophisticated techniques to bypass automatic detection mechanisms and harden manual analysis. Adversaries typical…
Are Trees Really Green? A Detection Approach of IoT Malware Attacks
Silvia Lucia Sanna, Diego Soi, Davide Maiorca +1
Nowadays, the Internet of Things (IoT) is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven…
A Risk Estimation Study of Native Code Vulnerabilities in Android Applications
Silvia Lucia Sanna, Diego Soi, Davide Maiorca +2
Android is the most used Operating System worldwide for mobile devices, with hundreds of thousands of apps downloaded daily. Although these apps are primarily written in Java and K…
Can you See me? On the Visibility of NOPs against Android Malware Detectors
Diego Soi, Davide Maiorca, Giorgio Giacinto +1
Android malware still represents the most significant threat to mobile systems. While Machine Learning systems are increasingly used to identify these threats, past studies have re…