Year: 2016
Type: article
Abstract: Computer worms have infected millions of computers since 1980s.For an incident handler or a forensic investigator, it is important to know whether the worm attack to the network has been initiated from multiple different sources or just from one node.In this paper, we study the problem of predicting the number of infectious nodes at each step of worm propagation, when the spread of a homogeneous random scanning worm happens.Knowledge of the number of infectious nodes might be a help in reconstructing the worm attack scene and in identifying the origins of worm propagation.In our approach, we assume Susceptible-Infectious-Removed (SIR) model for worm propagation, and propose two complementary models, i.e. deterministic Back-to-Origin model and stochastic Back-to-Origin Markov model, to investigate the above problem.In our Back-to-Origin models, we run the time backwards.We assume that we have prior knowledge of worm infection propagation parameters of SIR model.We also assume to have a snapshot in which the number of susceptible, infectious and removed nodes is known.Our deterministic Back-to-Origin model, is a new SIR model, where we define a susceptibility rate parameter.The stochastic Back-to-Origin Markov model is constructed based on the Continuous-Time-Markov-Chain.The number of infectious nodes at each time of worm propagation is predicted with our stochastic Markov model.We applied simulations to study the accuracy of our models.In numerical experiments of our stochastic Back-to-Origin Markov model, we investigate the probabilistic number of infectious nodes.Comparing to other approaches, the method of this paper requires a little information and a little assumptions, while it gives useful results.
Source: Journal of Computers
Authors: Tala Tafazzoli, Babak Sadeghiyan
Institution: Amirkabir University of Technology
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2579780686