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Machine Learning To Predict The Likelihood Of A Personal Computer To Be Infected With Malware, Maryam Shahini, Ramin Farhanian, Marcus Ellis
Machine Learning To Predict The Likelihood Of A Personal Computer To Be Infected With Malware, Maryam Shahini, Ramin Farhanian, Marcus Ellis
SMU Data Science Review
In this paper, we present a new model to predict the prob- ability that a personal computer will become infected with malware. The dataset is selected from a Kaggle competition supported by Mi- crosoft. The data includes computer configuration, owner information, installed software, and configuration information. In our research, sev- eral classification models are utilized to assign a probability of a machine being infected with malware. The LightGBM classifier is the optimum machine learning model by performing faster with higher efficiency and lower memory usage in this research. The LightGBM algorithm obtained a cross-validation ROC-AUC score of 74%. Leading factors …