As Android devices gain popularity and are increasingly used to perform sensitive tasks (e.g., online banking) they become an attractive target for cybercriminals. In this project we aim to better understanding the threat posed by Android malware, and to develop novel techniques to detect it and protect victim devices.
A Large-scale Temporal Measurement of Android Malicious Apps: Persistence, Migration, and Lessons Learned
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral ModelsEnrico Mariconti, Lucky Onwuzurike, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, and Gianluca Stringhini.
NDSS, 2017.
A Family of Droids: Analyzing Behavioral Model based Android Malware Detection via Static and Dynamic Analysis
Lucky Onwuzurike, Mario Almeida, Enrico Mariconti, Jeremy Blackburn, Gianluca Stringhini, and Emiliano De Cristofaro.
PST, 2018.
AndrEnsemble: Leveraging API Ensembles to Characterize Android Malware Families
Omid Mirzaei, Guillermo Suarez-Tangil, Juan Maria De Fuentes, Juan Tapiador, and Gianluca Stringhini.
ASIACCS, 2019.
Eight Years of Rider Measurement in the Android Malware EcosystemGuillermo Suarez-Tangil and Gianluca Stringhini.
TDSC 2020.