Towards Adaptive Big Data Cyber-attack Detection via Semantic Link Networks

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2016-TowardsAdaptiveBigDataCyber-attackDetectionviaSemanticLinkNetworks.pdf (225.73 KB)

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http://hdl.handle.net/11603/11349

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UMBC Information Systems Department
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Date

2016-07

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conference papers and proceedings
preprints
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Citation of Original Publication

George Karabatis, and Jianwu Wang, and Ahmed AlEroud, Towards Adaptive Big Data Cyber-attack Detection via Semantic Link Networks, The first Workshop of Mission-Critical Big Data Analytics (MCBDA), 2016.

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.

Subjects

Adaptive Cyber-attack Detection
Semantic Link Network
Big Data Platform
Streaming Data Analysis
UMBC High Performance Computing Facility (HPCF)

Abstract

As a core mechanism for cybersecurity, the ability to detect cyber-attacks is increasingly critical nowadays. There have been many types of network intrusion detection approaches, such as flow-based and packet-based, targeting single attack and multistage attack detection. Each approach has its own advantages and disadvantages. In this paper, we design an organic combination of these types of efforts into one comprehensive system. Furthermore, to deal with increasing volumes of network traffic and improve full packet analysis efficiency, we employ Spark Streaming platform for parallel detection.