Singular Spectrum Transform (SST) is a fundamental subspace analysis technique which has been widely adopted for solving change-point detection (CPD) problems in information security applications. However, the performance of a SST based CPD algorithm is limited to the lack of robustness to corrupted observations with large noises in practice. Based on the observation that large noises in practical time series are generally sparse, in this paper, we study a combination of Robust Principal Component Analysis (RPCA) and SST to obtain a robust CPD algorithm dealing with sparse large noises. The sparse large noises are to be eliminated from observation trajectory matrices by performing a low-rank matrix recovery procedure of RPCA. The noise-eliminated matrices are then used to extract SST subspaces for CPD. The effectiveness of the proposed method is demonstrated through experiments based on both synthetic and real-world datasets. Experimental results show that the proposed method outperforms the competing state-of-the-arts in terms of detection accuracy for time series with sparse large noises.
|Title of host publication||Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|Publication status||Published - 2018 Jul 16|
|Event||3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018 - Guangzhou, Guangdong, China|
Duration: 2018 Jun 18 → 2018 Jun 21
|Name||Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018|
|Other||3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018|
|Period||18/6/18 → 18/6/21|
Bibliographical noteFunding Information:
This work was partly supported by National NSF of China (No.61673059, No.61771051, No. U1713215).
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality