Markov chain Monte Carlo based internal attack evaluation for wireless sensor network

Wireless Sensor Networks (WSNs) consists of low-cost and multifunctional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. Normally, internal attack’s node behavioural pattern is different from the other neighbours, called “good nodes,” in a system even neighbour nodes can be attacked. In this paper, we have proposed a new approach for detecting internal attack by using Mrakov Chain Monte Carlo (MCMC). It is an efficient real time algorithm. It is good for sensor network as it operates with no or incomplete classification information. Our result shows the output of the internal attacker evaluation.