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Given how common mobile devices are in everyday life and how much financial and personal information is stored on them, identifying mobile malware has become a major worry. Protecting private data on smartphones and other devices from unauthorized access has becoming increasingly crucial. The author of this research uses a machine learning algorithm to identify malware in mobile apps in order to safeguard consumers from such apps. Reverse engineering must be used to obtain all of the app’s code before we can determine whether it is performing any nefarious tasks, such as sending SMS messages or copying contact information without authorization, in order to identify malware. We will identify the program as malicious if such behavior is present in the code. An application may contain more than 100 permissions (transact, API call signature, on Service Connected, bind Service, API call signature, attach Interface, API call signature, Service Connection, API call signature, android. OS. Binder, API call signature, SEND_SMS, Manifest Permission, Ljava.lang.Class.getCanonicalName, API call signature, etc.). Such permissions must be extracted from the code in order to create a characteristic dataset; if the app is properly authorized, we will assign a value of 1; otherwise, we will assign a value of 0. These characteristics determine whether the dataset program is classified as good ware or malware.

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Evaluating the Categorization of Android Malware Detection by Using Machine Learning Methodologies

  • 会议论文

  • 发表日期：

    2026年

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  • Ramesh Azmeera,
  • C. Syamsundar,
  • C. H. Swapna,
  • M. Kavya Kalyani

摘要

Given how common mobile devices are in everyday life and how much financial and personal information is stored on them, identifying mobile malware has become a major worry. Protecting private data on smartphones and other devices from unauthorized access has becoming increasingly crucial. The author of this research uses a machine learning algorithm to identify malware in mobile apps in order to safeguard consumers from such apps. Reverse engineering must be used to obtain all of the app’s code before we can determine whether it is performing any nefarious tasks, such as sending SMS messages or copying contact information without authorization, in order to identify malware. We will identify the program as malicious if such behavior is present in the code. An application may contain more than 100 permissions (transact, API call signature, on Service Connected, bind Service, API call signature, attach Interface, API call signature, Service Connection, API call signature, android. OS. Binder, API call signature, SEND_SMS, Manifest Permission, Ljava.lang.Class.getCanonicalName, API call signature, etc.). Such permissions must be extracted from the code in order to create a characteristic dataset; if the app is properly authorized, we will assign a value of 1; otherwise, we will assign a value of 0. These characteristics determine whether the dataset program is classified as good ware or malware.