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Android Malware Detection Using Genetic Algorithm

  • Unique Paper ID: 164483
  • Volume: 10
  • Issue: 12
  • PageNo: 1018-1023
  • Abstract:
  • This study presents an innovative approach for enhancing Android malware detection through a Genetic Algorithm (GA)-based optimized feature selection coupled with machine learning techniques. Leveraging the evolutionary principles of GA, the proposed method effectively identifies a subset of features from a large pool, maximizing the discriminative power while minimizing computational complexity. By integrating this feature selection mechanism with machine learning classifiers, the system achieves superior performance in distinguishing between, benign and malicious Android applications. Through extensive experimentation and evaluation using real-world datasets, the effectiveness of the proposed framework is demonstrated, showcasing significant improvements in detection accuracy, scalability, and efficiency compared to traditional methods. This research contributes to the advancement of Android security, offering a robust and adaptable solution for combating evolving malware threats in mobile ecosystems.
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  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 12
  • PageNo: 1018-1023

Android Malware Detection Using Genetic Algorithm

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