RAPSAMS: Robust affinity propagation clustering on static android malware stream-Reference-Cited by-同舟云学术

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RAPSAMS: Robust affinity propagation clustering on static android malware stream

Published:2022-04-10 Issue:15 Volume:34 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Katebi Matin¹,RezaKhani Afshin¹²,Joudaki Saba³^ORCID,Shiri Mohammad Ebrahim¹⁴

Affiliation:

1. Department of Computer Engineering, Borujerd Branch Islamic Azad University Borujerd Iran

2. Department of Computer Engineering Ayatollah Borojerdi University Borujerd Iran

3. Department of Computer Engineering, Khorramabad Branch Islamic Azad University Khorramabad Iran

4. Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6980

Reference47 articles.

1. MLDroid—Framework for Android malware detection using machine learning techniques;Mahindru A;Neural Comput Appl,2020

2. PerdisciR LeeW OllmannG.Method and system for network‐based detecting of malware from behavioral clustering; 2018. US Patent 9 948 671.

3. A Survey on Malware Detection Using Data Mining Techniques

4. AcamporaG BernardiML CimitileM TortoraG VitielloA.A fuzzy clustering‐based approach to study malware phylogeny. Proceedings of the 2018 IEEE International Conference on Fuzzy Systems (FUZZ‐IEEE); 2018:1‐8; IEEE.

5. An improved Android malware detection scheme based on an evolving hybrid neuro-fuzzy classifier (EHNFC) and permission-based features

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1. Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing;Computers, Materials & Continua;2023

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