Malware Detection Using Random Forest Algorithm

Malware Detection Using Random Forest Algorithm

P.Bhagyasree, S.Madhusudhan, Ch.Varaprasad ,T.Sai Sree Harsha, Dr. R.Rajender.

[1-4]B.Tech Student , 5  Professor & HOD, Department of CSSE ,LIET .

[1,2,3,4,5] Computer Science and System Engineering ,Lendi Institute of Engineering and Technology,Vizianagaram.

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Abstract:

This project presents a machine learning model for malware detection. The  model is trained on a dataset of malware and benign files using the Random  Forest algorithm.The model achieves an accuracy of 99% on the test dataset, which means that it can  correctly identify malware with a high degree of confidence. The model is also able to  detect new variants of malware that are not present in the training dataset.The model is a significant improvement over traditional malware detection  methods, which are often based on signature matching. Signature matching  can be effective against known malware, but it is not effective against new  malware variants.

KeyWords:Malwaredetection, RandomForest Algorithm, Datapreprocessing, Classification, Feature selection, accuracy.