An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features

An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features

With an increase in the number and complexity of malware, traditional malware detection methods such as heuristic-based and signature-based ones have become less adequate, leaving user applications vulnerable. Therefore, it is necessary to continue proposing and investigating new methods for Android...

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Bibliographic Details
Main Authors: Abdurraheem Joomye, Mee Hong Ling, Muhammed Basheer Jasser, Athirah Mohd Ramly, Kok-Lim Alvin Yau
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Android malware
machine learning
deep learning
temporal convolutional networks
dynamic analysis
Online Access:https://ieeexplore.ieee.org/document/10930493/
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