PUMA: Permission Usage to Detect Malware in Android

By Borja Sanz, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, Pablo García Bringas, Gonzalo Álvarez. CISIS/ICEUTE/SOCO Special Sessions, 2012

305 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a262f73c89d5fb429d60e3a2ed1b4d0168ee5cbd

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. Mauro Conti
2. Christian Platzer
3. Parvez Faruki
4. Matthias Neugschwandtner
5. Martina Lindorfer

In Information Systems

1. Mauro Conti
2. Christian Platzer
3. Matthias Neugschwandtner
4. Martina Lindorfer

In Signal Processing

1. Vijay Laxmi
2. Mauro Conti
3. Muttukrishnan Rajarajan
4. Vijay Ganmoor
5. Manoj Singh Gaur
6. Christian Platzer
7. Ammar Bharmal
8. Parvez Faruki

Citations in the corpus

• MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, background): “The remainder of related work on classifying Android malware based on static features evaluates their approaches on non-representative data sets: The data set of DroidMat [37] only contains 238 malware samples, PUMA [38] on the other hand, in addition to only learning from requested permissions, is ”
• Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, background): “Moreover, the users cannot differentiate between the necessity and its imperative misuse which may expose the for exploitation [38].”