A Literature Review on Android Mobile Malware Detection using Machine Learning Techniques
Author:
Affiliation:
1. Sathyabama Institute of Science and Technology,Department of Computer Science & Engineering,Chennai,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9752772/9753686/09753746.pdf?arnumber=9753746
Reference21 articles.
1. ENHANCED SOFT COMPUTING APPROACHES FOR INTRUSION DETECTION SCHEMES IN SOCIAL MEDIA NETWORKS
2. Apposcopy: semantics-based detection of Android malware through static analysis
3. PUMA: Permission Usage to Detect Malware in Android
4. DroidScope: Seamlessly reconstructing the os and dalvik semantic views for dynamic android malware analysis;yan,2012
5. No TiA Revolutionary Adaptive Alarm Management Systemtle;boulanger,0
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1. A Novel Feature Encoding Scheme for Machine Learning Based Malware Detection Systems;IEEE Access;2024
2. MobiPCR: Efficient, accurate, and strict ML-based mobile malware detection;Future Generation Computer Systems;2023-07
3. Android malware detection based on sensitive patterns;Telecommunication Systems;2023-02-20
4. Design of Machine Learning-Based Malware Detection Techniques in Smartphone Environment;2023 International Conference for Advancement in Technology (ICONAT);2023-01-24
5. A Context-Aware Android Malware Detection Approach Using Machine Learning;Information;2022-11-30
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