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

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.