A Survey of Intrusion Detection Schemes in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are currently used in many application areas including military applications, health related applications, control and tracking applications and environment and habitat monitoring applications. The harsh and unattended deployment of these networks along with their resource restrictions makes their security issue very important. Prevention-based security approaches like cryptography, authentication and key management have been used to protect WSNs from different kinds of attacks but these approaches are not enough to protect the network from insider attacks that may extract sensitive information even in the presence of the prevention-based solution. Detection-based approaches are then proposed to protect WSNs from insider attacks and act as a second line defense after the failure of the prevention-based approaches. Many intrusion detection schemes have been introduced for WSN in the literature. In this article, we present a survey of intrusion detection schemes in WSNs. First, we present the similar works and show their differences from this work. After that, we outline the fundamentals of intrusion detection in WSNs, describing the types of attacks and state the motivation for intrusion detection in WSNs. Then, we demonstrate the challenges of developing an ideal intrusion detection scheme for WSNs followed by the main requirements of a good candidate intrusion detection scheme. The state-of-the-art intrusion detection schemes are then presented based on the techniques used in each scheme and categorizing them into four main categories: rule-based, data mining and computational intelligence based, game theoretical based and statistical based. The analysis of each scheme in these categories is presented showing their advantages and drawbacks. By the end of each category, we state the general advantages and shortcomings of each category. The survey ends by recommending some important research opportunities in this field for future research.