In this paper3 , we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show t...
X. Rosalind Wang, Joseph T. Lizier, Oliver Obst, M...
—Principal component based anomaly detection has emerged as an important statistical tool for network anomaly detection. It works by projecting summary network information onto a...
In wireless sensor networks (WSNs), sensors' locations play a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number ...
The JiNao project at MCNC/NCSU focuses on detecting intrusions, especially insider attacks, against OSPF (Open Shortest Path First) routing protocol. This paper presents the imple...
D. Qu, Brain Vetter, Feiyi Wang, R. Narayan, Shyht...
In this paper the node-level decision unit of a self-learning anomaly detection mechanism for office monitoring with wireless sensor nodes is presented. The node-level decision uni...