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Logistic regression and dynamic feature selection based android malware detection approach

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DynamicFusion- Logistic Regression based Android Malware Detection Approach

Machine configuration

  • OS: Windows 10 64 bit
  • RAM: 8 GB
  • Processor: 11th Gen Intel(R) Core(TM) i5

Software requirements

  • Anaconda3 2021.11 (Python 3.9.7 64-bit)

Classifier

  • Logistic Regression

Dataset

  • The experiment is done using Malgenome-215 and Drebin-215 datasets.

Publication followed

  • S. Y. Yerima and S. Sezer, "DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection," in IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 453-466, Feb. 2019, doi: 10.1109/TCYB.2017.2777960.
  • Xu, R., Li, M., Yang, Z. et al. Dynamic feature selection algorithm based on Q-learning mechanism. Appl Intell 51, 7233–7244 (2021). https://doi.org/10.1007/s10489-021-02257-x

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