Anomaly IoT Node Detection Based on Local Outlier Factor and Time Series |
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Authors: | Fang Wang Zhe Wei Xu Zuo |
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Affiliation: | 1.School of Computer Science, Civil Aviation Flight University of China, Sichuan, 618307, China.
2 Anzina PTY Ltd., Sydney, NSW 2118, Australia. |
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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. |
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Keywords: | Local outlier factor time series Internet of Things anomaly node detection |
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