| CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Anomaly Detection of Complex Networks Based on Intuitionistic Fuzzy Set Ensemble |
| Jin-Fa Wang1, Xiao Liu1,2**, Hai Zhao1, Xing-Chi Chen1,3 |
1School of Computer Science and Engineering, Northeastern University, Shenyang 110819
2School of Biological and Biomedical Sciences, Durham University, Durham DH1 3LE, UK
3School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia
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| Cite this article: |
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Jin-Fa Wang, Xiao Liu, Hai Zhao et al 2018 Chin. Phys. Lett. 35 058901 |
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Abstract Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set (IFS)-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks. Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership, non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
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Received: 19 December 2017
Published: 30 April 2018
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| PACS: |
89.75.Fb
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(Structures and organization in complex systems)
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89.20.Ff
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(Computer science and technology)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Supported by the National Natural Science Foundation of China under Grant No 61671142, and the Fundamental Research Funds for the Central Universities under Grant No 02190022117021. |
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