Ensemble Machine Learning Approach for Android Malware Classification Using Hybrid Features

Abdurrahman Pektaş; Tankut Acarman

doi:10.1007/978-3-319-59162-9_20

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Ensemble Machine Learning Approach for Android Malware Classification Using Hybrid Features

Abdurrahman Pektaş Tankut Acarman

Year: 2017 Advances in intelligent systems and computing Pages: 191-200 Publisher: Springer Nature

DOI: 10.1007/978-3-319-59162-9_20

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Keywords:

Malware Computer science Android (operating system) Machine learning Artificial intelligence Scalability Feature extraction Data mining Computer security Operating system

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1.76

FWCI (Field Weighted Citation Impact)

Refs

0.85

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Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Malware Detection Techniques

Physical Sciences → Computer Science → Signal Processing

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Digital and Cyber Forensics

Physical Sciences → Computer Science → Information Systems

Ensemble Machine Learning Approach for Android Malware Classification Using Hybrid Features

Metrics

Citation History

Topics

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