Apposcopy: semantics-based detection of Android malware through static analysis

By Yu Feng, Saswat Anand, Işıl Dillig, A. Aiken. SIGSOFT FSE, 2014

489 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/737c1927a221bf1864af8622d6d4a85733325cb6

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

1. Lorenzo Cavallaro
2. Wajih Ul Hassan
3. Wei Yang
4. Patrick Traynor
5. Wei Cheng
6. Tao Xie
7. Rosli Bin Salleh
8. William Enck

In Information Systems

1. Yves Le Traon
2. Jacques Klein
3. Damien Octeau
4. Alexandre Bartel
5. Wei Yang
6. Mike Papadakis
7. Tégawendé F. Bissyandé
8. Patrick Traynor

In Signal Processing

1. Lorenzo Cavallaro
2. Damien Octeau
3. Kimberly Tam
4. Jacques Klein
5. Yves Le Traon
6. Alexandre Bartel
7. Tégawendé F. Bissyandé
8. Wei Yang

Citations in the corpus

• Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps (2017, methodology): “Specifically, we follow the evaluation conducted for Apposcopy [17] to pick the results of seven well-known anti-virus vendors ( i.e., AVG, Symantec, ESET, Dr. Web, Kaspersky, Trend Micro, and McAfee) and label an app as malicious if more than half of the seven suggest that the app is malicious.”
• *droid (2016, methodology): “…framework contains thousands of methods, most static analysis tools that need to analyze flow through the framework use manually described models of the behavior described as stub functions [Gordon et al. 2015; Fritz et al. 2014; Wei et al. 2014; Kim et al. 2012; Feng et al. 2014; Lu et al. 2012].”
• Static analysis of android apps: A systematic literature review (2017, methodology): “Taint Analysis AAPL [61], Amandroid [68], Anadroid [69], AndroidLeaks [80], Apparecium [83], AppAudit [85], AppCaulk [86], AppContext [88], Apposcopy [92], AppSealer [94], Bastani et al.”
• The Evolution of Android Malware and Android Analysis Techniques (2017, background): “An encouraging number of frameworks such as Apposcopy [Feng et al. 2014] are making efforts to overcome limitations like low levels of obfuscation but still do not cope with higher ones.”
• You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, methodology): “In traditional approaches, static analysis [95], [92], [46] and dynamic analysis [24], [41], [34] have been used to analyze and detect malware.”