Open Access
Research Article
GCANQGM : Design of an efficient graph convolutional AHP neural network with Deep Q generative adversarial network for classification of android malwares
Harshal Devidas Misalkarharshal.misalkar2019@vitbhopal.ac.inSchool of Computing Science & Engineering VIT Bhopal University KothrikalanSehore, Madhya Pradesh, 466114, IndiaView full profile → , *Pon HarshavardhananCorresponding authorpon.harshavardhanan@vitbhopal.ac.inSchool of Computing Science Engineering and Artificial Intelligence VIT Bhopal University KothrikalanSehore, Madhya Pradesh, 466114, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 01 May 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2250
- Pages:
- 1603–1611
Abstract
Android malware is also changing fast and is becoming problematic to conventional static and dynamic detection techniques that tend to ignore real-time API communications, log patterns, and access patterns. The current paper suggests a new Android malware detection and classification system based on the combination of Graph Convolutional Neural Networks (GCN) and multi-source data fusion comprising of logcat outputs, API calls, permissions, device logs, and debug messages. The GCN outputs are used to optimize Analytical Hierarchy Process (AHP) weights and Deep Q-GAN (DQ-GAN) is used to improve malware classification. The proposed model is also more precise, accurate, recall, AUC, and specificity than detection delay, showing a better adaptability and strength to emerging Android malware threats.
Keywords
Subject Classifications
68M25
References
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