  Ensemble learning for anomaly detection of data structured into complex
network has been barely studied due to the inconsistent performance of complex
network characteristics and lack of inherent objective function. In this paper,
we propose the IFSAD, a new two-phase ensemble method for anomaly detection
based on intuitionistic fuzzy set, and applies it to the abnormal behavior
detection problem in temporal complex networks. First, it constructs the
intuitionistic fuzzy set of single network characteristic which quantifies the
degree of membership, non-membership and hesitation of each of 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 an 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
inconsistent of multiple characteristics. Finally, the score function and
precision function are used to sort the fused IFS. Finally we carried 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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