The Evolution of Android Malware and Android Analysis Techniques
Author:
Affiliation:
1. Information Security Group, Royal Holloway, University of London
2. Department of Computer System and Technology, University of Malaya
3. Information Security Group, Royal Holloway, University of London, Egham Hill, United Kingdom
Abstract
Funder
UK EPSRC
Ministry of Science, Technology and Innovation
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Link
https://dl.acm.org/doi/pdf/10.1145/3017427
Reference180 articles.
1. Going Native: Using a Large-Scale Analysis of Android Apps to Create a Practical Native-Code Sandboxing Policy
2. DroidBarrier
3. A. Amamra C. Talhi and J. Robert. 2012. Smartphone malware detection: From a survey towards taxonomy. In Malicious and Unwanted Software (MALWARE). 10.1109/MALWARE.2012.6461012 A. Amamra C. Talhi and J. Robert. 2012. Smartphone malware detection: From a survey towards taxonomy. In Malicious and Unwanted Software (MALWARE). 10.1109/MALWARE.2012.6461012
4. Applying machine learning classifiers to dynamic Android malware detection at scale
5. Automated concolic testing of smartphone apps
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