MODELLING MALWARE PROPAGATION ON THE INTERNET OF THINGS USING AN AGENT BASED APPROACH ON COMPLEX NETWORKS

Jordanian Journal of Computers and Information Technology

Shedden Masupe¹  Mandu Jeffrey²  Karanja Evanson Mwangi³ 

[1] BITRI, Botswana;Department of Electrical Engineering, University of Botswana Gaborone, Botswana;Faculty of Engineering, University of Botswana Gaborone, Botswana;

关键词: internet of things;    agent-based modelling and simulation;    modelling malware propagation;    large-scale-free networks;    deep-reinforcement learning;   

DOI  :  10.5455/jjcit.71-1568145650

来源: DOAJ

【 摘 要 】

Malware threat is a major hindrance to efficient information exchange on the Internet of Things (IoT). Modelling malware propagation is one of the most imperative applications aimed at understanding mechanisms for protecting the Internet of Things environment. Internet of Things can be realized using agent-based modelling over complex networks. In this paper, a malware propagation model using agent-based approach and deep-reinforcement learning on scale free network in IoT (SFIoT) is assiduously detailed. The proposed model is named based on transition states as Susceptible-Infected-Immuned-Recovered-Removed (SIIRR) that represents the states of nodes on large-scale complex networks. The reliability of each node is investigated using the Mean Time To Failure (MTTF). The factors considered for MTTF computations are: degree of a node, node mobility rate, node transmission rate and distance between two nodes computed using Euclidean distance. The results illustrate that the model is comparable to previous models on effects of malware propagation in terms of average energy consumption, average infections at time (t), node mobility and propagation speed.

【 授权许可】

Unknown