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Dynamic Malicious Node Detection With Semi‐supervised Multivariate Classification In Cognitive Wireless Sensor Networks

Hongjun Dai, Huabo Liu, Z. Jia
Published 2015 · Computer Science

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Usually, wireless sensor networks are distributed massively with a number of nodes in an open large‐scale environment, and they are vulnerable to malicious attacks because the communications change dynamically and unpredictably. In this paper, we present a detection method based on multivariate classification to find out the malicious sensor nodes. It learns the features of a few type‐known node, classifies them with dynamical multivariate classification, and then establishes the sample space of all sensor nodes in the network activities to deduce the malicious nodes. The experiment results show that as long as the value of sensor node preferences and the number of active sensor nodes is stable, the false detection rate is stabilized below 0.5%. This proves that the algorithm can be used to the cognitive wireless sensor networks widely. Copyright © 2014 John Wiley & Sons, Ltd.
This paper references
An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications
Murad A. Rassam (2013)
Denial of Service in Sensor Networks
A. Wood (2002)
Hierarchical Trust Management for Wireless Sensor Networks and its Applications to Trust-Based Routing and Intrusion Detection
F. Bao (2012)
Some properties of a classification system for multivariate life distributions
D. Roy (2001)
A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection
J. Hu (2009)
Hatzinakos D. Ensemble empirical mode decomposition for time Goel G (2014)
Security in wireless sensor networks
V. C. Giruka (2008)
GPSR: greedy perimeter stateless routing for wireless networks
B. Karp (2000)
A Machine Learning Based Reputation System for Defending Against Malicious Node Behavior
R. Akbani (2008)
Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction
S. De Vito (2012)
Environmental Wireless Sensor Networks
P. Corke (2010)
Balancing lifetime and classification accuracy of wireless sensor networks
Kush R. Varshney (2013)
Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA
J. Joseph (2011)
Ad-hoc on-demand distance vector routing
C. Perkins (1999)
Insider Attacker Detection in Wireless Sensor Networks
F. Liu (2007)
Anomaly Detection in Wireless Sensor Networks
Miao Xie (2013)
Packet traffic: a good data source for wireless sensor network modeling and anomaly detection
Qinghua Wang (2011)
MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks
André L. L. de Aquino (2014)
ZoneTrust: Fast Zone-Based Node Compromise Detection and Revocation in Wireless Sensor Networks Using Sequential Hypothesis Testing
Jun-Won Ho (2012)
Ensemble Empirical Mode Decomposition for time series prediction in wireless sensor networks
G. Goel (2014)
Cognition in Wireless Sensor Networks: A Perspective
G. Vijay (2011)
Topological detection on wormholes in wireless ad hoc and sensor networks
Dezun Dong (2009)
Malicious node detection in wireless sensor networks
Waldir Ribeiro Pires Júnior (2004)
Modeling and Unsupervised Classification of Multivariate Hidden Markov Chains With Copulas
Nicolas Brunel (2010)
Distributed Detection in Mobile Access Wireless Sensor Networks under Byzantine Attacks
M. Abdelhakim (2014)
Some properties of a classification system for multivariate life distributions
D. Roy (2001)
Information theoretic framework of trust modeling and evaluation for ad hoc networks
Y. Sun (2006)
Outlier Detection Techniques for Wireless Sensor Networks: A Survey
Y. Zhang (2010)

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