Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 44 references
Computer Science
TL;DR
FUADroid is proposed, a static malware detection method that fuses structural and statistical-semantic views and achieves strong detection performance and exhibits improved robustness under cross-year evaluation settings.
The results have shown how well-engineered static features coupled with overfitting-aware ensemble design can give robust results of multi-class malware classification in the absence of dynamic traces, to develop additional resilience from obfuscation and runtime-evasive threats.
H. Lamkuche, Mannat Pal· Journal of Computer Virology...· 0 citations
FusionDroid is proposed, a lightweight multimodal Android malware detection framework that integrates permission-based static features, permission co-occurrence graph representations, and engineered runtime behavioral features through probability-level ensemble fusion that can improve Android malware detection while pr...
This work compiled a dataset of benign and malicious APKs and performed static analysis to extract features such as code structure, permissions, and manifest file content, without executing the apps, and used an LLM to generate high-level functional descriptions of APKs.
Saraga Sakthidharan, S. Anagha, Dincy R. Arikkat et al.· International Journal of Inf...· 2 citations
An intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance is presented, providing an effective and reliable solution for securing Android devices against evolving malware threats.
Aishwarya Eklar, G.Rajini· International Journal of Eng...· 0 citations
Mirae is presented, a multimodal agent collaborative framework for dynamic Android malware detection that dynamically collects multimodal runtime evidence and employs ReAct-based specialized agents to analyze complementary behavioral views and achieves strong zero-shot generalization against Android malware concept dri...
Xueying Zeng, Youquan Xian, Yanze Li et al.· 0 citations
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and nine machine learning an...
Ahmed Sabbah, Mohammed F. Kharma, Radi Jarrar et al.· Journal of Cybersecurity and...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
