In this paper, we classify applications into three categories according to their APIs and permissions: Benign, Suspicious, and Malicious. To achieve that, we ...
In this paper, malware analysis is classified into 3-level based on API and permissions. In Level 1, there are 19 categories of basic behaviors that can occur ...
This system could make the users aware of security risks by providing them insights on the mobile application behaviors, which allows the users to make an ...
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The solution utilizes permissions in addition to intent and API requests to ensure a 97.87% success rate. Nevertheless, the approach is limited to a dataset of ...
These datasets may contain static features, such as API calls, intents, and permissions, or dynamic features, like logcat errors, shared memory, and system ...
Oct 8, 2023 · Therefore, this study employed ML algorithms to classify Android applications into malware or goodware using permission and application ...
Therefore, this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface ( ...
In this paper, we propose an effective approach to attenuate the problem of Android malware detection using static code analysis based models.
Oct 23, 2024 · In this paper, we put forward a systematic approach to generate an Android permission-based dataset using static analysis.
The paper proposes a framework for analysing and classifying a malicious application to certain families relying on the permissions used, and the results ...
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