Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model

Qin Yu, Jibin Lyu, Lirui Jiang and Longjiang Li

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 7, 2181256

Abstract: As WSNs gain popularity, they are becoming more and more necessary for traffic anomaly detection. Because worms, attacks, intrusions, and other kinds of malicious behaviors can be recognized by traffic analysis and anomaly detection, WSN traffic anomaly detection provides useful tools for timely reaction and appropriate prevention in network security. In the paper, we improve exploitation of GM(1,1) model to make traffic prediction and judge the traffic anomaly in WSNs. Based on our systematical researches on the characteristics of WSN traffic, the causes of WSN abnormal traffic, and latest related research and development, we better exploit the GM(1,1) model following four guidelines: using a sliding window to determine historical data for modeling, optimizing initial value of one-order grey differential equation, making traffic prediction by short step exponential weighted average method, and judging whether the traffic of the next moment is abnormal by Euclidean distance. Then, we propose a traffic anomaly detection algorithm for WSNs based on the improved exploitation of GM(1,1) model. Simulation results and comparative analyses demonstrate that our proposed WSN traffic anomaly detection algorithm can reduce the undetected rate and has better anomaly detection accuracy than traditional traffic anomaly detection algorithms.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/155014772181256 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:7:p:2181256

DOI: 10.1177/155014772181256

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().