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Cluster Computing

, Volume 22, Supplement 4, pp 8309–8317 | Cite as

Anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment

  • Chen YangEmail author
Article

Abstract

In recent years, there are more and more abnormal activities in the network, which greatly threaten network security. Hence, it is of great importance to collect the data which indicate the running statement of the network, and distinguish the anomaly phenomena of the network in time. In this paper, we propose a novel anomaly network traffic detection algorithm under the cloud computing environment. Firstly, the framework of the anomaly network traffic detection system is illustrated, and six type of network traffic features are consider in this work, that is, (1) number of source IP address, (2) number of source port number, (3) number of destination IP address, (4) number of destination port number, (5) Number of packet type, and (6) number of network packets. Secondly, we propose a novel hybrid information entropy and SVM model to tackle the proposed problem by normalizing values of network features and exploiting SVM detect anomaly network behaviors. Finally, experimental results demonstrate that the proposed algorithm can detect anomaly network traffic with high accuracy and it can also be used in the large scale dataset.

Keywords

Anomaly network traffic detection Information entropy measurement Cloud computing Support vector machine Quantum behaved particle swarm optimization 

Notes

Acknowledgements

The authors are very thankful to the editors and anonymous reviewers for providing very thoughtful comments which have lead to an improved version of this paper. This work was supported by the Natural Science Foundation of China (No. 61572033) and also supported by General program of humanistic and social science research in Anhui provincial higher education promotion plan (TSSK2016B27); 2017 General topic capital of online educational research fund by online education research center of Department of Education(2017YB101) and Key topics of national education information technology research(176120003).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Modern education technology centerAnhui Polytechnic UniversityAnhuiChina

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