The Evolution of Android Malware and Android Analysis Techniques
By Kimberly Tam, Ali Feizollah, Nor Badrul Anuar, Rosli Bin Salleh, Lorenzo Cavallaro. ACM Computing Surveys, 2017
343 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Software, Signal Processing
View Paper: https://www.semanticscholar.org/paper/30c7daed94dcf51c33624c2f9d4f9d4175f555a6
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In Computer Networks and Communications
In Software
In Signal Processing
Citations in the corpus
- A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “…141 references; 124 methods employing the static analysis of Android apps were extensively reviewed in Reference [30] with 188 references; 36 Android malware detection tools with a further extended view in the static, dynamic and hybrid analysis were presented in Reference [31] with 181 references.”
- A Survey on Malware Detection Using Data Mining Techniques (2017, background): “This trend will undoubtedly continue [Tam et al. 2017].”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “This information can be stolen and exploited by malicious apps installed on smartphones for ill-conceived purposes [405].”