An intrusion detection method for wireless sensor network based on mathematical morphology

Abstract

Security issue in Internet of Things (IoTs) has long been the topic of extensive research in the last decade. Data encryption and authentication are the most common two methods to address the security issues in IoTs. However, these efforts are ineffective in detecting the diverse malicious attacks, especially in intrusion detection. Comparatively, very few attentions have been paid for detecting intrusive nodes in IoTs research. Therefore, in this paper, we derive an innovative method called granulometric size distribution (GSD) method based on mathematical morphology for detecting malicious attack in IoTs, such as intrusion detection. We successfully generate GSD clusters to directly monitor the number of active nodes in a wireless sensor network because the GSD curves are similar when the number of active nodes in a wireless sensor network is fixed. Link Quality Indicator data of each node are utilized as the network parameters in this method. The results show the effectiveness in intrusion detection. Copyright © 2015 John Wiley & Sons, Ltd.

BLTM: Beta and LQI Based Trust Model for Wireless Sensor Networks

“…Three different clustering methods are applied to the collected data and the clustering results reflect the number of the active nodes in the network. A Granulometric Size Distribution (GSD) method is proposed in [23] based on the erosion method of mathematical morphology, and the LQI data is collected from the network and the GSD method is used to cluster the data. According the GSD clusters, the number of active nodes can be directly monitored.…”