Anomaly Detection of Complex Networks Based on Intuitionistic Fuzzy Set Ensemble

TL;DR

This paper introduces IFSAD, a novel ensemble method using intuitionistic fuzzy sets for anomaly detection in complex networks, effectively handling noisy features and characteristic inconsistencies.

Contribution

The paper proposes a two-phase ensemble approach with a Gaussian-based membership function and fuzzy fusion to improve anomaly detection in complex networks.

Findings

IFSAD outperforms state-of-the-art methods in experiments

The Gaussian membership function effectively quantifies feature hesitation

Fuzzy fusion reduces characteristic inconsistency

Abstract

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.