Reduction of the feature space for the detection of attacks of wireles sensor networks

Author(s):  
Victoria Korzhuk ◽  
Ilya Shilov ◽  
Julia Torshenko
2014 ◽  
Vol 926-930 ◽  
pp. 1886-1889
Author(s):  
Bo Tian ◽  
Dian Hong Wang ◽  
Fen Xiong Chen ◽  
Zheng Pu Zhang

This paper presents a new algorithm for the detection of abnormal events in Wireless Sensor Networks (WSN). Abnormal events are sets of data points that correspond to interesting patterns in the underlying phenomenon that the network monitors. This algorithm is inspired from time-series data mining techniques and transforms a stream of sensor readings into an Extension Temporal Edge Operator (ETEO) of time series pattern representation, and then extracts the three eigenvalue of each sub-pattern, that is, patterns length, patterns slope and patterns mean to map time series to feature space, and finally uses local outlier factor to detect abnormal pattern in this feature space. Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in WSN.


Author(s):  
Mohammad S. Obaidat ◽  
Sudip Misra

2012 ◽  
Author(s):  
Tom Busey ◽  
Chen Yu ◽  
Francisco Parada ◽  
Brandi Emerick ◽  
John Vanderkolk

2008 ◽  
Vol 1 (1) ◽  
pp. 20-41
Author(s):  
G. ANASTASI ◽  
M. CONTI ◽  
M. DI FRANCESCO ◽  
E. GREGORI ◽  
A. PASSARELLA

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