11 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Tiezhu Sun, Nadia Daoudi, Kisub Kim +3
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graph…
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Tiezhu Sun, Kevin Allix, Kisub Kim +5
The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). Central to applying ML to software artifacts (like source or ex…
JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis
Jordan Samhi, Jun Gao, Nadia Daoudi +6
Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities.…
A First Look at Android Applications in Google Play related to Covid-19
Jordan Samhi, Kevin Allix, Tegawendé F. Bissyandé +1
Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pa…
Learning to Catch Security Patches
Arthur D. Sawadogo, Tegawendé F. Bissyandé, Naouel Moha +4
Timely patching is paramount to safeguard users and maintainers against dire consequences of malicious attacks. In practice, patching is prioritized following the nature of the cod…