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Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning by Islam, Rejwana (author);Sayed, Moinul Islam (author);Saha, Sajal (author);Hossain, Mohammad Jamal (author);Masud, Md Abdul (author) is a Computer Science article available to read on EtoBox.

What is Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning about?

The majority of smartphones on the market run on the Android operating system. Security has been a core concern with this platform since it allows users to install apps from unknown sources. With thousands of apps being produced and launched daily, malware detection using Machine Learning (ML) has attracted significant attention compared to traditional detection techniques. Despite academic and commercial efforts, developing an efficient and reliable method for classifying malware remains challenging. As a result, several datasets for malware analysis have been generated and made available during the past ten years. These datasets may contain static features, such as API calls, intents, and permissions, or dynamic features, like logcat errors, shared memory, and system calls. Dynamic analysis is more resilient when it comes to code obfuscation. Though binary classification and multi-classification have been carried out in recent studies, the latter provides valuable insight into the nature of malware. Because each malware variant operates differently, identifying its category might help prevent it. Using the well-known ensemble ML approach called weighted voting, this study perform

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Author

Islam, Rejwana (author);Sayed, Moinul Islam (author);Saha, Sajal (author);Hossain, Mohammad Jamal (author);Masud, Md Abdul (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)