The proactivity of Perceptron derived algorithms in malware detection

Abstract

As the number and impact of online threats increases exponentially, the automatic classification of malware becomes increasingly important in the antivirus business. The heavy use of machine learning in this field raises the following question: "How much will a trained machine-learning model resist in time against the ever-changing malware binary code?" In this paper we present a study of proactivity in malware detection using Perceptron derived algorithms. We gathered an industrial quantity of both malicious and benign files, then we trained a series of classifiers on nine months' worth of data and discuss the behavior of the obtained models tested on the next fourteen weeks. We conclude with the result analysis and recommendations for the practical use of this technique in real life scenarios.