By Topic

Distributed PCA-based anomaly detection in wireless sensor networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Livani, M.A. ; Dept. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran ; Abadi, M.

Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). In this paper, we propose a distributed energy-efficient approach for detecting anomalies in sensed data in a WSN. The anomalies in sensed data can be caused due to compromised or malfunctioning nodes. In the proposed approach, we use distributed principal component analysis (DPCA) and fixed-width clustering (FWC) in order to establish a global normal profile and to detect anomalies. The process of establishing the global normal profile is distributed among all sensor nodes. We also use weighted coefficients and a forgetting curve to periodically update the established normal profile. We demonstrate that the proposed distributed approach achieves comparable accuracy compared to a centralized approach, while the communication overhead in the network and energy consumption is significantly reduced.

Published in:

Internet Technology and Secured Transactions (ICITST), 2010 International Conference for

Date of Conference:

8-11 Nov. 2010