Abstract: Original approaches to building a system for detection of computer viruses are discussed in this work. The actuality of developing and implementing an effective classifier for both known and previously unknown viruses (zero-day) is grounded. A general approach to construction of a classifier for static virus detection using machine learning, based on analysis of recent research, is outlined in the paper. The results of new research are presented. The choice of the final aggregation model - Stack classifier - is grounded.

Keywords: classifier, detection, computer viruses, machine learning

ACM Classification: E4. Coding and Information Theory

Link:

КЛАССИФИКАТОР ДЛЯ СТАТИЧЕСКОГО ОБНАРУЖЕНИЯ КОМПЬЮТЕРНЫХ ВИРУСОВ, ОСНОВАННЫЙ НА МАШИННОМ ОБУЧЕНИИ (Classifier for static detection of computer viruses, based on machine learning)

Евгений Путин, Адиль Тимофеев

http://www.foibg.com/ijitk/ijitk-vol08/ijitk08-02-p01.pdf