A Hybrid CNN–BiLSTM Framework Optimized with Bayesian Search for Robust Android Malware Detection

A Hybrid CNN–BiLSTM Framework Optimized with Bayesian Search for Robust Android Malware Detection

With the rapid proliferation of Android smartphones, mobile malware threats have escalated significantly, underscoring the need for more accurate and adaptive detection solutions. This work proposes an innovative deep learning hybrid model that combines Convolutional Neural Networks (CNNs) with Bidi...

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Bibliographic Details
Main Author: Ibrahim Mutambik
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Systems
Subjects:
Android malware detection
malware detection
CNN–BiLSTM hybrid model
Mutual Information
Bayesian Optimization
Online Access:https://www.mdpi.com/2079-8954/13/7/612
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