@Article{cmc.2020.09774,
AUTHOR = {Fang Wang, Zhe Wei, Xu Zuo},
TITLE = {Anomaly IoT Node Detection Based on Local Outlier Factor and Time Series},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1063--1073},
URL = {http://www.techscience.com/cmc/v64n2/39346},
ISSN = {1546-2226},
ABSTRACT = {The heterogeneous nodes in the Internet of Things (IoT) are relatively weak in 
the computing power and storage capacity. Therefore, traditional algorithms of network 
security are not suitable for the IoT. Once these nodes alternate between normal behavior 
and anomaly behavior, it is difficult to identify and isolate them by the network system in 
a short time, thus the data transmission accuracy and the integrity of the network function 
will be affected negatively. Based on the characteristics of IoT, a lightweight local outlier 
factor detection method is used for node detection. In order to further determine whether 
the nodes are an anomaly or not, the varying behavior of those nodes in terms of time is 
considered in this research, and a time series method is used to make the system respond 
to the randomness and selectiveness of anomaly behavior nodes effectively in a short 
period of time. Simulation results show that the proposed method can improve the 
accuracy of the data transmitted by the network and achieve better performance.},
DOI = {10.32604/cmc.2020.09774}
}