Collective Anomaly Detection Using Big Data Distributed Stream Analytics

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The majority of today's data is creating in a form of streams by diverse application domains and the sizes of these data are becoming very large in order to detect unusual events or predict intelligent decision. This often poses a significant challenging task to process and discover unusual events from such dynamic behaviours. Importantly, real time detection in streaming application is a critical concern to detect anomalous event, this is due to a potential infinite size of the data and an evolving behaviour of the event stream. In this paper, we proposed a novel distributed real time Collective Anomaly detection by; a) defining event stream snapshot model, b) designing a novel distributed Collective event stream detection, c) evaluates the experimental result in terms of the accuracy of the detection rates and the detection processing runtime performance. The experimental evaluation is demonstrated that our method is capable to detect a collection of events from high volumes of unbounded streams in real-time.

Original languageEnglish
Title of host publicationProceedings - 2018 14th International Conference on Semantics, Knowledge and Grids, SKG 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages188-195
Number of pages8
ISBN (Electronic)9781728104416
ISBN (Print)9781728104423
DOIs
Publication statusPublished - 2 May 2019
Event14th International Conference on Semantics, Knowledge and Grids - Guangzhou, China
Duration: 12 Sep 201814 Sep 2018
Conference number: 14

Publication series

NameProceedings of the International Conference on Semantics, Knowledge and Grids
PublisherIEEE
ISSN (Print)2325-0623

Conference

Conference14th International Conference on Semantics, Knowledge and Grids
Abbreviated titleSKG 2018
CountryChina
CityGuangzhou
Period12/09/1814/09/18

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