An Automatic Updating Perceptron-Based System for Malware Detection

In the increasing number of online threats and shape-shifting malware, the use of machine learning techniques has a good impact. To keep the efficiency of these techniques, the training and adaptation schedule must be constant. In this paper we study the behaviour of an automatic updating perceptron, with variable training frequency and using as input samples with increasing freshness. Other variable parameters are the features set and training set dimensions. The collected samples, clean and malicious are from the last year. We conclude with the observed optimal parameters which can be used to obtain a good proactivity.