Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Deep Learning

  • Unique Paper ID: 159655
  • Volume: 9
  • Issue: 12
  • PageNo: 348-354
  • Abstract:
  • Malware has become a serious threat to Android devices due to the increasing popularity of these devices. In this paper, we propose a novel method for Android malware detection using genetic algorithm based optimized feature selection and deep learning. Our approach aims to select the most relevant features for detecting Android malware using genetic algorithm based optimization. The selected features are then used to train a deep learning model using CNN and LSTM algorithm for accurate malware detection. We evaluate the performance of our proposed method using a dataset of Android malware and benign apps. The results show that our approach achieves high accuracy in detecting Android malware, outperforming existing methods.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{159655,
        author = {Smitha S and Sneha N and Kavya B M and Manasa G R and Heena Kousar},
        title = {Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {348-354},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159655},
        abstract = {Malware has become a serious threat to Android devices due to the increasing popularity of these devices. In this paper, we propose a novel method for Android malware detection using genetic algorithm based optimized feature selection and deep learning. Our approach aims to select the most relevant features for detecting Android malware using genetic algorithm based optimization. The selected features are then used to train a deep learning model using CNN and LSTM algorithm for accurate malware detection. We evaluate the performance of our proposed method using a dataset of Android malware and benign apps. The results show that our approach achieves high accuracy in detecting Android malware, outperforming existing methods.},
        keywords = {Android malware detection, genetic algorithm, feature selection, deep learning},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 12
  • PageNo: 348-354

Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Deep Learning

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