Adaptive Anomaly Detection on Network Data Streams

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Title: Adaptive Anomaly Detection on Network Data Streams
Authors: Riddle-Workman, E
Evangelou, M
Adams, N
Item Type: Conference Paper
Abstract: As the number of cyber-attacks increases, there has been increasing emphasis on developing complementary methods of detection to the existing signature-based approaches. This work builds upon a previously discovered persistent structure within the Los Alamos National Laboratory network data sources, to develop a regression based streaming anomaly detection mechanism that can adapt to the network behaviour over time. The methodology has also been applied to a new data set of the same network to assess the extent of its pertinence in time.
Issue Date: 8-Nov-2018
Date of Acceptance: 15-Sep-2018
URI: http://hdl.handle.net/10044/1/64846
Publisher: IEEE
Journal / Book Title: IEEE
Copyright Statement: This paper is embargoed until publication.
Conference Name: IEEE Conference on Intelligence and Security Informatics (ISI) 2018
Publication Status: Accepted
Start Date: 2018-11-08
Finish Date: 2018-11-10
Conference Place: Miami, FL, USA
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Mathematics
Statistics
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care



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