A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code
By Lei Cen, Christopher S. Gates, Luo Si, Ninghui Li. 2015
135 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Software, Safety, Robustness & Interpretability, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Earl T. Barr
- Charles Sutton
- Premkumar T. Devanbu
- Sakir Sezer
- Miltiadis Allamanis
- Uri Alon
- Eran Yahav
In Software
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Michael Pradel
- Rishabh Singh
- Marc Brockschmidt
- Sakir Sezer
In Safety, Robustness & Interpretability
In Signal Processing
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Sakir Sezer
- Suleiman Y. Yerima
- Mohammed K. Alzaylaee
- Eran Yahav
Citations in the corpus
- A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “ derived from the software engineering process, such as process measures, requirement traceability, commit logs. Additionally, research in malware detection is related to probabilistic models of code [18, 36]. Another active area at the intersection machine learning and programming language research”
- DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Arp et al. (2014) , Aafer et al. (2013) , Yerima et al. (2015a) , Fan et al. (2017) , Yerima et al. (2015b) , Kang et al. (2016b) , Cen et al. (2015) , Westyarian et al. (2015) and Kang et al. (2016a) are few examples of detection methods using static analysis.”
- A Survey of App Store Analysis for Software Engineering (2017, unknown): “[34], Liu et al.”