A Novel Evidential Evaluation for Internal Attacks with Dempster-Shafer Theory in WSN

Abstract

Wireless Sensor Network (WSN) 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 behavioral pattern is different from the other neighbors, called "good nodes," in a system even neighbor nodes can be attacked. In this paper, we use Dempster-Shafer theory (DST) of combined multiple evidences to identify the malicious or internal attacks in a WSN. Moreover, it gives a numerical procedure for fusing together multiple pieces of evidences from unreliable neighbor with higher degree of conflict reliability.