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Discrete Dynamics in Nature and Society
Volume 2017, Article ID 5743801, 9 pages
https://doi.org/10.1155/2017/5743801
Research Article

A Dynamic Programming Model for Internal Attack Detection in Wireless Sensor Networks

1School of Computer Science and Control Engineering, North University of China, Taiyuan, Shan’xi 030051, China
2School of Instrument and Electronics, North University of China, Taiyuan, Shan’xi 030051, China

Correspondence should be addressed to Qiong Shi; nc.ude.cun@1460gnoiqihs

Received 10 March 2017; Accepted 9 May 2017; Published 1 June 2017

Academic Editor: Lu-Xing Yang

Copyright © 2017 Qiong Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Internal attack is a crucial security problem of WSN (wireless sensor network). In this paper, we focus on the internal attack detection which is an important way to locate attacks. We propose a state transition model, based on the continuous time Markov chain (CTMC), to study the behaviors of the sensors in a WSN under internal attack. Then we conduct the internal attack detection model as the epidemiological model. In this model, we explore the detection rate as the rate of a compromised state transition to a response state. By using the Bellman equation, the utility for the state transitions of a sensor can be written in standard forms of dynamic programming. It reveals a natural way to find the optimal detection rate that is by maximizing the total utility of the compromised state of the node (the sum of current utility and future utility). In particular, we encapsulate the current state, survivability, availability, and energy consumption of the WSN into an information set. We conduct extensive experiments and the results show the effectiveness of our solutions.