Android permissions: a perspective combining risks and benefits

By Bhaskar Pratim Sarma, Ninghui Li, Christopher S. Gates, Rahul Potharaju, Cristina Nita-Rotaru, Ian M. Molloy. ACM Symposium on Access Control Models and Technologies, 2012

351 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/365859840ba0b560a6e82e0e808ee26a7734af51

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. Konrad Rieck
3. Daniel Arp
4. Hugo Gascón
5. Cristina Nita-Rotaru
6. Malte Hubner
7. Luo Si
8. Mauro Conti

In Information Systems

1. Konrad Rieck
2. Daniel Arp
3. Hugo Gascón
4. Cristina Nita-Rotaru
5. Luo Si
6. Mauro Conti
7. Rosli Bin Salleh
8. Ninghui Li

In Signal Processing

1. Lorenzo Cavallaro
2. Konrad Rieck
3. Daniel Arp
4. Hugo Gascón
5. Cristina Nita-Rotaru
6. Malte Hubner
7. Vijay Laxmi
8. Luo Si

Citations in the corpus

• DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “As Kirin and RCP both consider only a subset of the requested permissions, they have obvious limitations in detecting malicious applications.”
• A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks (2014, neutral): “[45] and Peng et al.”
• Using probabilistic generative models for ranking risks of Android apps (2012, methodology): “To identify critical permissions, we start from a list of 26 permissions identified in [24] as critical.”
• Structural detection of android malware using embedded call graphs (2013, methodology): “The performance measure is depicted along three recently proposed methods for static detection of Android malware that we have also tested on our dataset: Kirin [10], RCP [35] and the approach proposed by Peng et. al in [31], where we implement the latter using 0.00 0.02 0.04 0.06 0.08 0.10 False Po”
• Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, methodology): “used permissions is an important risk identification technique [86], [87].”
• Evaluation of machine learning classifiers for mobile malware detection (2014, neutral): “Sarma et al. (2012) published a research work in 2012 in which they analyzed permissions of Android files.”
• A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code (2015, methodology): “Experimental results show that these models significantly outperform prior approaches in [1], [2] using the area under curve (AUC) for the receiver operating characteristic (ROC) curve as evaluation metric.”
• The Evolution of Android Malware and Android Analysis Techniques (2017, background): “Additionally, Sarma et al. [2012] analyzed 150,000 Android applications in 2012 and found that 93.38% of malicious apps required network access while only 68.50% of normal apps did so.”