Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection

Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection

As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signatur...

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Bibliographic Details
Main Authors: Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, Shakirat A. Salihu, Fatima E. Usman-Hamza, Peter O. Sadiku, Ghaniyyat B. Balogun, Zubair O. Alanamu
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Applied Sciences
Subjects:
android
malware detection
machine learning
meta-learner
Online Access:https://www.mdpi.com/2076-3417/12/9/4664

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https://www.mdpi.com/2076-3417/12/9/4664

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