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

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. [ABSTRACT FROM AUTHOR]