Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model
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 rea...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/155014772181256 |
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| _version_ | 1832556822212378624 |
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| author | Qin Yu Jibin Lyu Lirui Jiang Longjiang Li |
| author_facet | Qin Yu Jibin Lyu Lirui Jiang Longjiang Li |
| author_sort | Qin Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-750c447c6da64d00a7084a45e2f821aa |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-750c447c6da64d00a7084a45e2f821aa2025-02-03T05:44:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-07-011210.1177/155014772181256Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) ModelQin Yu0Jibin Lyu1Lirui Jiang2Longjiang Li3 School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China Department of Computer Science, University of Southern California (USC), Los Angeles, CA 90089, USA School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaAs 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.https://doi.org/10.1177/155014772181256 |
| spellingShingle | Qin Yu Jibin Lyu Lirui Jiang Longjiang Li Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model International Journal of Distributed Sensor Networks |
| title | Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model |
| title_full | Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model |
| title_fullStr | Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model |
| title_full_unstemmed | Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model |
| title_short | Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model |
| title_sort | traffic anomaly detection algorithm for wireless sensor networks based on improved exploitation of the gm 1 1 model |
| url | https://doi.org/10.1177/155014772181256 |
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