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Activity Number: 141 - Statistical Analysis of Cyber-Security Data
Type: Invited
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Royal Statistical Society
Abstract #322169 View Presentation
Title: Modeling Computer Network Data Using Markov Modulated Poisson Processes
Author(s): Mark Briers*
Companies: Alan Turing Institute
Keywords: Markov Modulated Poisson Process ; Cyber Security ; Particle Filters ; Sequential Monte Carlo ; Expectation Maximization

There are a number of latent factors, such as the presence of a user executing processes on a client machine, that determines the communication rate of machines across a computer network. A Markov Modulated Poisson Process (MMPP) is a Poisson process whose rate ?(t) evolves according to a continuous time Markov chain, making it a natural choice for modelling computer network data. In this talk, we will demonstrate that an MMPP is an appropriate model for network traffic data rates. In undertaking such an analysis, we will introduce a numerically stable estimation procedure for the Expectation step when performing parameter estimation using the Expectation Maximization algorithm.

Authors who are presenting talks have a * after their name.

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