Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android
By Vaibhav Rastogi, Zhengyang Qu, Jedidiah McClurg, Yinzhi Cao, Yan Chen. 2015
32 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, 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 Artificial Intelligence
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Marco Melis
- Konrad Rieck
- Igino Corona
In Computer Networks and Communications
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Konrad Rieck
- Igino Corona
- Ambra Demontis
- Marco Melis
In Signal Processing
- Battista Biggio
- Giorgio Giacinto
- Davide Maiorca
- Fabio Roli
- Ambra Demontis
- Konrad Rieck
- Igino Corona
- Marco Melis
Citations in the corpus
- Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, background): “that is downloaded or decrypted at runtime, or code that is thoroughly obfuscated [17], [23], [24], [31], [35], [36], [47].”
- Static analysis of android apps: A systematic literature review (2017, background): “[89], MalloDroid [91], MIGDroid [33], Nyx [43], ORBIT [154], PaddyFrog [98], Redexer [107], SADroid [112], SEFA [115], SIG-Droid [52], SmartDroid [118], Uranine [127], vLens [44], Wang et al.”
- Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis (2017, methodology): “, the network, during runtime, either by modifying the device OS (TaintDroid [13]), the platform libraries (Phosphor [8]), or the app under analysis (Uranine [33]).”