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1 ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
1 International Journal of Applied Mathematics and Computer Science

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Investigation of agent-based simulation of malicious software

Chumachenko D., Yakovlev S.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

2016

Vol. 5, No 4

61--67

Epidemics of malicious software are actual problem and network worms are one of the most important issues. Identifying trends in network worm distribution, finding the factors that influence the spread of the Internet worm will help to identify the effective preventive and precautionary measures to prevent epidemics of malicious software. To solve the problem of the development of advanced security mechanisms against network worms, different approaches to modeling the spreading of worms have been studied. Deterministic models of propagation of computer viruses in a heterogeneous network, taking into account its topological and architectural features have been analyzed and improved. Agent-based model of network worm propagation have been developed. Simulated model is based on epidemic approach to modeling. SAIDR structure of agent-based model has been used for simulation of malicious software of “network worm” type. A comparative study of developed mathematical models has been conducted. Comparative graphs of the dependence of the infected nodes number on the time of the computer system functioning in the propagation of the epidemic have been built. Research carried out by the example of the Code Red worm propagation.

A repeated imitation model with dependence between stages: Decision strategies and rewards

Villacorta P. J., Pelta D. A.

International Journal of Applied Mathematics and Computer Science

2015

Vol. 25, no. 3

617--630

Adversarial decision making is aimed at determining strategies to anticipate the behavior of an opponent trying to learn from our actions. One defense is to make decisions intended to confuse the opponent, although our rewards can be diminished. This idea has already been captured in an adversarial model introduced in a previous work, in which two agents separately issue responses to an unknown sequence of external inputs. Each agent’s reward depends on the current input and the responses of both agents. In this contribution, (a) we extend the original model by establishing stochastic dependence between an agent’s responses and the next input of the sequence, and (b) we study the design of time varying decision strategies for the extended model. The strategies obtained are compared against static strategies from theoretical and empirical points of view. The results show that time varying strategies outperform static ones.