Supervised-ML-Projects
Malware Detection Using Supervised ML
In this project supervised machine learning is used for malware detection in the system. Primary data has been used for this project. Dynamic malware analysis is performed, where malware is analyzed after executing it in the malware analysis lab. Flare VM on Windows 10 distribution is used as the sandbox. The log files are obtained by running both malware and goodware on Flare vm. Data is extracted from these log files using an NLP technique called bag of words and labeled afterward. This is followed by model training and evaluation. RandomForest, Decision Tree, Logistic Regression and SVM are the four different algorithms which were used to train the model. Among them, Random Forest gave the highest accuracy of 99.99806%
code 1 (please refer to this link for code1) https://colab.research.google.com/drive/18wwIZtXoN6fTaATTAi61iUz9eBcDdGft?authuser=2