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Data Analytics of a Honeypot System Based on a Markov Decision Process Model by Lidong Wang; Randy Jones; Terril C. Falls is a book available to read on EtoBox.

What is Data Analytics of a Honeypot System Based on a Markov Decision Process Model about?

A honeypot system can play a significant role in exposing cybercrimes and maintaining reliable cybersecurity. Markov decision process (MDP) is an important method in systems engineering research and machine learning. The data analytics of a honeypot system based on an MDP model is conducted using R language and its functions in this paper. Specifically, data analytics over a finite planning horizon (for an undiscounted MDP and a discounted MDP) and an infinite planning horizon (for a discounted MDP) is performed, respectively. Results obtained using four kinds of algorithms (value iteration, policy iteration, linear programming, and Q-learning) are compared to check the validity of the MDP model. The simulation of expected total rewards for various states is implemented using various transition probability parameters and various transition reward parameters.

Author

Lidong Wang; Randy Jones; Terril C. Falls

Publisher

Springer International Publishing : Imprint: Springer

Published

2022

Language

EN

ISBN

9783030820824

Subjects

Management, Engineering, Mathematics