Hybrid Android Malware Detection and Classification Using Deep Neural Networks

Hybrid Android Malware Detection and Classification Using Deep Neural Networks

Abstract This paper presents a deep learning-based framework for Android malware detection that addresses critical limitations in existing methods, particularly in handling obfuscation and scalability under rapid mobile app development cycles. Unlike prior approaches, the proposed system integrates...

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Bibliographic Details
Main Authors: Muhammad Umar Rashid, Shahnawaz Qureshi, Abdullah Abid, Saad Said Alqahtany, Ali Alqazzaz, Mahmood ul Hassan, Mana Saleh Al Reshan, Asadullah Shaikh
Format: Article
Language:English
Published: Springer 2025-03-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Malware
Android malware
Artificial neural networks
Machine learning
Online Access:https://doi.org/10.1007/s44196-025-00783-x
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https://doi.org/10.1007/s44196-025-00783-x

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