Malware Predictor using Machine Learning Techniques

Authors

  • Jaevier A. Villanueva
  • Roben Juanatas
  • Luisito L. Lacatan

Abstract

Malware has always been a threat to the computer world, but with fast growth in the use of the internet, malware severely affects the computer world. Malware predictors and detectors are critical tools in defense against malware. The existing malware detectors and predictors have been created, the effectiveness of these detectors and predictors depend upon the techniques being used.  This study is specifically, addressed the following objectives: (1) propose a model to predict malware behavior using machine activity data; (2) apply the random forest algorithm in predicting malicious behavior. In this study, applied research is being used; this is the stage in the life cycle of the study in which we recognize how well we have used our knowledge to solve a pressing problem and to produce predictable results. In the proposed work of this research, a useful machine learning model using a random forest is developed and implemented with a malware data base. The proposed multi-layer machine learning model is used for training and predictive malware analysis on multiple parameters, including error factor, accuracy rate, and overall performance. The result of the model from the evaluation measures provides a high accuracy rate and a lesser mean absolute error value. There are very few parameters in the random forest as well, and these can be optimized using generalization theory without having to separate validation set during training.

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Published

2020-01-27

Issue

Section

Articles