Genetic Algorithm Modeling Approach for Mobile Malware Evolution Forecasting

Mobile malware is a relatively new but constantly increasing threat to information security and modern means of communication. Mobile malware evolution speedup is highly expected due to the increase of the SmartPhone and other mobile device market and malware development shift from vandalism to economic aspect. Forecasting evolution tendencies is important for development of countermeasure techniques and prevention of malware epidemic outbreaks. Existing malware propagation models mainly concentrate on malware epidemic consequences modeling, i.e. forecasting the number of infected computers, simulating malware behavior or economic propagation aspects and are based only on current malware propagation strategies or oriented to other malware types. In this article we propose using the genetic algorithm modeling approach for mobile malware evolution forecasting. Genetic algorithm is selected as a modeling tool taking into consideration the efficiency of this method while solving optimization, modeling problems with large solution space and successful application for other malware type evolution forecasting. The model includes the genetic algorithm description, operating conditions, chromosome that describes mobile malware characteristic and the fitness function for propagation strategy evolution evaluation. Model was implemented and tested on the MATLAB platform.