Open Access

ARTICLE

Anomaly IoT Node Detection Based on Local Outlier Factor and Time Series

Fang Wang^{1, *}, Zhe Wei¹, Xu Zuo²

1 School of Computer Science, Civil Aviation Flight University of China, Sichuan, 618307, China.
2 Anzina PTY Ltd., Sydney, NSW 2118, Australia.

* Corresponding Author: Fang Wang. Email: .

Computers, Materials & Continua 2020, 64(2), 1063-1073. https://doi.org/10.32604/cmc.2020.09774

Received 18 January 2020; Accepted 16 April 2020; Issue published 10 June 2020

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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Citations