MStream: Fast Anomaly Detection in Multi-Aspect Streams-Reference-Cited by-同舟云学术

MStream: Fast Anomaly Detection in Multi-Aspect Streams

Published:2021-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Web Conference 2021
language:
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Author:

Bhatia Siddharth¹,Jain Arjit²,Li Pan³,Kumar Ritesh⁴,Hooi Bryan¹

Affiliation:

1. National University of Singapore, Singapore

2. Indian Institute of Technology, Bombay, India

3. Purdue University, USA

4. Indian Institute of Technology, Kharagpur, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442381.3450023

Reference81 articles.

1. Leman Akoglu Hanghang Tong and Danai Koutra. 2015. Graph Based Anomaly Detection and Description: A Survey. Data mining and knowledge discovery(2015). Leman Akoglu Hanghang Tong and Danai Koutra. 2015. Graph Based Anomaly Detection and Description: A Survey. Data mining and knowledge discovery(2015).

2. Elisa Bertino Evimaria Terzi Ashish Kamra and Athena Vakali. 2005. Intrusion detection in RBAC-administered databases. In ACSAC. Elisa Bertino Evimaria Terzi Ashish Kamra and Athena Vakali. 2005. Intrusion detection in RBAC-administered databases. In ACSAC.

3. Petko Bogdanov Christos Faloutsos Misael Mongiovì Evangelos E Papalexakis Razvan Ranca and Ambuj K Singh. 2013. NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks. In SDM. Petko Bogdanov Christos Faloutsos Misael Mongiovì Evangelos E Papalexakis Razvan Ranca and Ambuj K Singh. 2013. NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks. In SDM.

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