Title: Heterogeneous big data fusion in distributed networking systems for anomaly detection and localisation

Authors: Yuan Zuo; Xiaozhou Zhu; Jiangyi Qin; Wen Yao

Addresses: National Innovation Institution of Defence Technology, PLA Academy of Military Science, Beijing, China ' National Innovation Institution of Defence Technology, PLA Academy of Military Science, Beijing, China ' National Innovation Institution of Defence Technology, PLA Academy of Military Science, Beijing, China ' National Innovation Institution of Defence Technology, PLA Academy of Military Science, Beijing, China

Abstract: An efficient anomaly detection and localisation mechanism is crucial for achieving high-quality network services. In particular, learning-based methods have recently been developed to achieve this goal by discovering helpful information from a massive amount of heterogeneous network data. However, heterogeneous data from various network components lead to significant challenges and an unexpected burden for analysis. The distributed scale of networking systems challenges data integrity and knowledge retrieval due to the separation of coupled functions over the distributed system. In this article, an insightful survey is performed by thoroughly reviewing recent academic and industrial contributions regarding anomaly detection and localisation. To tackle the issues, we propose a new framework to effectively learn informative representations of heterogeneous data and fuse this information for efficient anomaly detection and localisation. Furthermore, a case study is presented for anomaly detection and localisation through learning data representations and performing heterogeneous data fusion.

Keywords: data heterogeneity; distributed networking systems; anomaly detection; anomaly localisation; machine learning.

DOI: 10.1504/IJSN.2022.125511

International Journal of Security and Networks, 2022 Vol.17 No.3, pp.220 - 229

Received: 09 Jul 2021
Accepted: 17 Jul 2021
Published online: 13 Sep 2022 *

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