Skip to content

A Machine Learning approach for detecting malicious executable files with 98% accuracy

License

Notifications You must be signed in to change notification settings

DipakSalunke/malicious_exe_file_detection_using_ml

Repository files navigation

Machine-Learning-approach-for-Malware-Detection

Check it out :
https://dipak-scanexe.herokuapp.com/

you can use a QR scanner app on your phone, or some camera apps :

A Machine Learning approach for classifying a file as Malicious or Legitimate.

This approach tries out 6 different classification algorithms before deciding which one to use for prediction by comparing their results. Different Machine Learning models tried are, Linear Regression, RandomForest, DecisionTree, Adaboost, Gaussian, Gradient Boosting.

In order to test the model on an unseen file, it's required to extract the characteristics of the given file. Python's pefile.PE library is used to construct and build the feature vector and a ML model is used to predict the class for the given file based on the already trained model.

Dependencies

  • pandas pip install pandas
  • numpy pip install numpy
  • pickle pip install pickle
  • scipy pip install scipy
  • scikit pip install -U scikit-learn

About

A Machine Learning approach for detecting malicious executable files with 98% accuracy

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published