A CHAOS-BASED AND ENSEMBLED METHOD FOR OUTLIER DETECTION

With the advent of the Big Data era, anomaly detection has become an important tool for screening the validity of data. Many well-established distance-based or correlation-based anomaly detection methods are widely used for various structured and feature-based datasets with the increasing size of data. However, different method strategies have different focuses, leading to large deviations in anomaly detection results for the same dataset using different methods, which poses a great challenge to anomaly detection research. In this paper, a new strategy is proposed for anomaly detection using integrated methods. By using a two-stage process of the sliding window aggregation method, the strategy uses a multi-model anomaly scoring method and a uniform quantitative criterion filtering to obtain a suitable anomaly scoring.