@conference{Karapistoli201349,

title = {SRNET: A real-time, cross-based anomaly detection and visualization system for wireless sensor networks},

author = { E. Karapistoli and P. Sarigiannidis and A.A. Economides},

url = {https://www.researchgate.net/publication/262347500_SRNET_A_real-time_cross-based_anomaly_detection_and_visualization_system_for_wireless_sensor_networks?_sg=KDdCBpLRuPqrORtOxHP4P38HAPGocREt6hya_JuOcjZ4M1vhmZ-JbGRQWfXjdL1x1Y-q1yQxIBrvu1w},

doi = {10.1145/2517957.2517964},

year  = {2013},

date = {2013-01-01},

journal = {ACM International Conference Proceeding Series},

pages = {49-56},

abstract = {Security concerns are a major deterrent in many applications wireless sensor networks are envisaged to support. To date, various security mechanisms have been proposed for these networks dealing with either Medium Access Control (MAC) layer or network layer security issues, or key management problems. Security visualization is the latest weapon that has been added in the arsenal of a security officer who is tasked with detecting network anomalies by analyzing large amounts of audit data. This paper proposes a novel security visualization system for analyzing and detecting complex patterns of sensor network attacks, called SRNET. Both selective forwarding and jamming attacks are identified through visualizing and analyzing network traffic data on multiple coordinated views, namely the multidimensional crossed view, the crossed view perspective, and the track area view. Through simulations, we demonstrate that SRNET is able to help detect and further identify the root cause of the aforementioned sensor network attacks. Copyright 2013 ACM.},

keywords = {Cross-based anomaly detection and visualization, Detection of selective forwarding and jamming attacks, Wireless sensor network security visualization},

pubstate = {published},

tppubtype = {conference}

}