Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams

TL;DR

This paper introduces a novel framework for anomaly detection in uncertain pseudoperiodic data streams, utilizing uncertainty pre-processing, pattern recognition, and classification to improve accuracy and efficiency.

Contribution

It presents a new framework that effectively handles uncertainty in data streams for anomaly detection, combining pre-processing, pattern recognition, and classification methods.

Findings

High accuracy of anomaly detection demonstrated on real datasets

Efficient uncertainty pre-processing improves detection performance

Pattern recognition enhances computational efficiency

Abstract

Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.