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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
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

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pp. 1603–1611Vol. 47Issue 5-AMay 2026DOI: 10.47974/JIOS-2250XML
Received:
01 Apr 2025
Published Online:
23 Apr 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

Android threat intelligenceBehavioral malware classificationGraph-based deep learningReinforcement learning for cybersecurityDynamic API call analysis

Subject Classifications

68M25

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