Passive Defense

Passive Defense

A Hybrid Framework for Android Malware Detection Using an Improved Particle Swarm Optimization and Fuzzy Kernel Support Vector Machine

Document Type : Original Article

Author
Aliakbar Tajari Siahmarzkooh
Assistant Professor, Department of Computer Science, Golestan University, Gorgan, Iran
10.47176/pd.2026.1584
Abstract
This paper presents a novel hybrid framework for Android malware detection that integrates an Improved Particle Swarm Optimization (IPSO) algorithm with a Fuzzy Kernel Support Vector Machine (FKSVM). The primary objective is to overcome the challenges of traditional methods, including high feature dimensionality, premature convergence, and the inability to model the inherent uncertainty in malware data. The IPSO algorithm, leveraging a dynamic inertia weight and a random mutation operator, prevents premature convergence and selects an optimal feature subset. Subsequently, the FKSVM model effectively manages data uncertainty through a novel fuzzy kernel. Evaluation of the proposed framework on the CIC-AndMal2020 dataset demonstrates that the method achieves a binary classification accuracy of 99.98% and an extremely low false alarm rate of 0.01%. These results clearly indicate the superior performance of the proposed framework compared to existing methods in terms of accuracy, speed, and robustness against adversarial attacks, thereby confirming its effectiveness.
Keywords
Malware Detection
Android
Particle Swarm Optimization
Support Vector Machine
Fuzzy Kernel
Feature Selection
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Passive Defense
Volume 17, Issue 2 - Serial Number 66
Serial number 66. Summer 2026
Summer 2026
Pages 75-90

  • Receive Date 08 October 2025
  • Revise Date 04 January 2026
  • Accept Date 13 July 2026
  • Publish Date 23 July 2026