Android Malware Detection Using ML & DL
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Machine and Deep Learning Application. Applying big data learning techniques for a malware classification problem. Code: https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0 Acknowledgments: Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
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Android Malware Detection Using ML & DL
- 1. Android Malware Machine and Deep Learning Application Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS) Indraneel C. Dabhade 2021
- 2. Topics. Use Case Analysis Data Exploration Project Framework Feature Exploration Machine Learning Technique Deep Learning Technique Introduction Assumptions and Future Work Acknowledgements Appendix
- 3. Introduction Questions answered. Why have I chosen a specific method for data quality assessment? Why have I chosen a specific method for feature engineering? Why have I chosen a specific algorithm? Why have I chosen a specific framework? Why have I chosen a specific model performance indicator?
- 4. Introduction Project Snapshot. Machine Learning Technique: Random Forest. Accuracy : ~50.0% Deep Learning with optimization using Stochastic Gradient Descent. Accuracy : ~92.0% Technology Infrastructure: IBM Cloud Pak Python 3.7 with Apache Spark 3 TensorFlow 2.0
- 6. Dataset
- 7. Initial Pruning and Cleaning Eliminate Binomial Variables. Delete rows with null types.
- 8. Feature Engineering Feature Engineering Machine Learning Variance Threshold of 1000. Filter Features Vector Assembler Pipeline Execution Standard Scaler Normalizer Principal Component Analysis.
- 9. Deep Learning Variance Threshold of 1000. Filter Features Feature Pipeline Execution Standard Scaler Principal Component Analysis. Feature Engineering Feature Engineering Label Pipeline Execution MinMax Scaler One Hot Encoding
- 10. Data Exploration Feature Engineering Feature Engineering Feature Engineering Feature Engineering Number of Features: ~59 Random Sampling for Rows: 5000 Train: Test Split = 70: 30 Sample Feature Correlation Matrix Heatmap
- 11. Deep Learning Feature Engineering Random Forest Random Forest Bins: 100 Number of Trees: 10 Maximum Depth of a Tree: 4
- 12. Deep Learning 5 Layer Sequential Neural Network ……. ……. ……. ……. 32 64 128 512 ……. 42
- 15. Assumptions and Future Work Not able to run Keras2DML. Sampling unbiased representation of population. Further exploration of features using bias vs variance tradeoffs. Current project focuses on the ‘Dynamic Analysis’ dataset. Can explore ‘Static Analysis’ datasets. Acknowledgments Like to thank family and friends for their support, the Canadian Institute for Cybersecurity (CIC) and the Canadian Centre for Cyber Security (CCCS), and IBM Cloud Pak for the infrastructure. All models are wrong, but some are useful – George Box.
- 16. Appendix Python code for the Machine Learning execution of this project. (MachineLearning.ipynb) Python code for the Deep Learning execution of this project. (DeepLearning.ipynb) Data used for the project. (df.csv) Data for statistical analysis. (Sample.csv) Python code for Statistical Analysis. (DataStatistics.ipynb) https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
- 17. Thank you for your time.