Malware Detection using Dynamic Hybrid ANFIS-Whales Approach

This repository contains the implementation of a Dynamic Hybrid ANFIS-Whales approach for predicting mobile malware. The code is based on the research article "DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware". The implementation includes a comparison with the Whales algorithm.

Directory Structure

• /Data
  • CreateData.m
  • LoadData.m
  • Main Data.mat
  • Normalize.m
  • _Data.mat
• /Fis
  • Fis_GetParameters.m
  • Fis_Initialization.m
  • Fis_SetParameters.m
• /Methods
  • Algorithm_ACOR.m
  • Algorithm_AIA.m
  • Algorithm_DE.m
  • Algorithm_FA.m
  • Algorithm_GA.m
  • Algorithm_HS.m
  • Algorithm_ICA.m
  • Algorithm_KA.m
  • Algorithm_PSO.m
  • Algorithm_TLBO.m
  • Algorithm_WOA.m
• /Show
  • GetOutPut_OPT_Fis.m
  • Main_Train_Test_Plot_.m
  • PlotResults_Train_Test.m
• AddPath.m
• Fitness.m
• Main.m
• README.md

Usage

To use this project, follow these steps:

1. Clone the repository: git clone https://github.com/Ilia-Abolhasani/malware-detection.git
2. Open MATLAB and navigate to the project directory.
3. Make sure to add the necessary paths by running AddPath.m.
4. Run the Main.m script to execute the malware detection process.
5. The script will load the data, perform feature normalization, initialize the Fuzzy Inference System (FIS), and apply various optimization algorithms for malware detection.
6. The results, including performance metrics and plots, will be generated and displayed.

Dataset

The dataset used for training and testing the malware detection models is included in this repository in the "Data" folder. It contains the following files:

• CreateData.m: MATLAB script for creating the dataset.
• LoadData.m: MATLAB script for loading the dataset.
• Main Data.mat: Main dataset file.
• Normalize.m: MATLAB script for normalizing the dataset.
• _Data.mat: Additional dataset file (generated dataset).

Please refer to these files in the "Data" folder for further details on the dataset and its structure.

References

If you use this code or dataset in your research or project, please consider citing the following paper: "DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware"

License

This project is licensed under the MIT License.

Contact

For any inquiries or issues regarding this project, please contact Ilia-Abolhasani.