Prediction of Malware Propagation in Complex Networks Using the Susceptible Infected Recovered Model

Author

Nyakusendwa, Tendero

Title

Prediction of Malware Propagation in Complex Networks Using the Susceptible Infected Recovered Model

Abstract

The aim of this study was to develop a system and assess the use of the Susceptible Infected Recovered(SIR) model in predicting the propagation and spreading of malicious software in complex computer networks. The study had three(3) research objectives, the first one was to evaluate different models and techniques used for malware propagation prediction in a computer network, the second objective was to design and implement a simulated environment which predicts malware propagation prediction in a complex computer network using the Susceptible Infected Recovered (SIR) model. The last and third objectives was to evaluate the effectiveness of Susceptible Infected Recovered(SIR) model in predicting malware propagation prediction in a complex computer network. Therefore, to this end, the researcher managed to review vast literature to do with the study in question, from whence the author acquired insight on the different variables which can be used for creating the virus propagation simulation. The author went on to study literature on the mathematical compartments which are effective for this task and chose the SIR model thus satisfying the first objective. The SIR model was employed by the virus propagation simulation, using the prototyping software development model. The simulation process for predicting malware propagation using the SIR model takes inputs of various parameters such as the total number of nodes and edges in two networks, propagation probability, total number of iterations in the two networks, and edge ratio between networks. The simulation process has significant implications for network security as it allows for the prediction of malware propagation and provides insights that can inform the development of more effective strategies to prevent and mitigate malware attacks in large-scale networks. The simulation yielded substantial and satisfactory outcomes concerning the anticipation of malware dissemination in intricate networks. The results indicated that the overall quantity of nodes and edges within the networks exerted a notable influence on the duration of malware propagation. In essence, as the number of nodes and edges increased, the time taken for malware to propagate grew longer. Moreover, it was discovered that the probability of propagation had a significant impact on the speed at which malware spread within the network.

Date

2023

Publisher

BUSE

Keywords

Malware Propagation

Complex Networks

Susceptible Infected Recovered Model

Prediction

Supervisor

N/A