Worm infectious probability distribution with back‐to‐origin model. Issue 13 (19th September 2017)

• Record Type: Journal Article
• Authors: Tafazzoli, Tala; Sadeghiyan, Babak
• Is Part Of: IET communications. Volume 11:Issue 13(2017)
• Page Start: 2101
• Page End: 2109
• Publication Date: 2017-09-19
• Subjects: belief networks -- Markov processes -- invasive software -- Internet; worm infectious probability distribution -- back‐to‐origin model -- backward time -- infectious nodes -- Bayesian networks -- stochastic back‐to‐origin models -- stochastic back‐to‐origin Markov model -- worm infection parameters -- removed nodes -- probability distribution -- epidemic spread -- Internet; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2016.0835
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : In this paper, we consider the problem of estimating the infection probability of nodes in backward time steps of worm propagation with Bayesian networks. The infection probability of a node at each time depends on the out‐degree of the node and the number of infectious nodes at that time. It is assumed that we have prior knowledge of worm infection parameters and also the number of susceptible, infectious and removed nodes at a time of worm propagation. The out_degree of a node is needed at each time step for estimation. It also needs to learn a degree distribution model over time based on the observation of historical out_degree of nodes in the network when the spread of worm happens. We applied simulations to study the accuracy of our probability distribution. The results of simulation indicate that the probability distribution predicts the infection probability of nodes at each prior time step with high accuracy. This method can be used to infer the origin and worm propagation path. This method has low storage and computational requirements, and also less limiting assumptions compared to other methods of estimating the origin nodes of epidemic spread.