Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

An Anomaly Detection Method for Stateful Stream Processing System

Authors
Guanghui Chang, Lu Zhao, Jun Liu, Peizhen Li
Corresponding Author
Guanghui Chang
Available Online March 2017.
DOI
https://doi.org/10.2991/msam-17.2017.44How to use a DOI?
Keywords
stream processing; complex event processing; kpca; svm
Abstract
The SSPS (stateful stream processing system) is a distributed complex event processing system implemented by the framework of stream processing system. However, due to the nature of uncertainty, randomness and burstiness of the data stream and the complexity of complex event processing, the SSPS faces great challenges. In order to address the problem, this paper analyzes the problems of SSPS, on this basis, an anomaly detection method based on FKPCA-SVM is proposed. Experimental results show that the proposed method can efficiently and reliably detect SSPS, and ensure the system can operate normally.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.44How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Guanghui Chang
AU  - Lu Zhao
AU  - Jun Liu
AU  - Peizhen Li
PY  - 2017/03
DA  - 2017/03
TI  - An Anomaly Detection Method for Stateful Stream Processing System
BT  - 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 196
EP  - 199
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.44
DO  - https://doi.org/10.2991/msam-17.2017.44
ID  - Chang2017/03
ER  -