An extrinsic random-based ensemble approach for android malware detection
Author:
Affiliation:
1. Department of Information & Communication Systems Engineering, University of Aegean, Karlovasi Samos, Greece
2. European Commission, Joint Research Centre (JRC), Ispra, Italy
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/09540091.2020.1853056
Reference35 articles.
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2. Android malware genome project (n.d.). Retrieved November 8, 2020, from http://www.malgenomeproject.org/
3. Arp, D., Spreitzenbarth, M., Gascon, H. & Rieck, K. (2014). Drebin: Effective and explainable detection of android malware in your pocket. In Proceedings of the Network and Distributed System Security Symposium (NDSS). The Internet Society.
4. EC2: Ensemble Clustering and Classification for Predicting Android Malware Families
5. Contagio mobile (n.d.). Retrieved November 8, 2020, from http://contagiominidump.blogspot.com/.
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