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A Detection Method for Anomaly Flow in Software Defined Network

delete2018-01-01
delete42
delete
OA
AI
H
Huijun Peng
Z
Zhe Sun
X
Xuejian Zhao
S
Shuhua Tan
Z
Zhixin Sun *
DOI：10.1109/ACCESS.2018.2839684delete
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Abstract

Abstract

En 中文
As a new type of network structure, the Software Defined Network (SDN) provides a new solution for networkfiow management and optimization, which has made the accurate detection of anomaly SDNfiows a hot research topic. This paper presents an SDN-basedfiow detection method, builds structures for detecting anomaly SDNfiows and performs classification detection on thefiows using the double P-value of transductive confidence machines for K-nearest neighbors algorithm. The experimental results show that the algorithm proposed achieves a lower false positive rate, higher precision, and better adaptation to the SDN environment than do other algorithms of the same type.
Keywords:
Intrusion detection
detection algorithms
nearest neighbor searches
SDN
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Journal

IEEE Access cover
IEEE Access
IF:
3.6
Papers:
9.8W
Citations:
29.4W

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