Towards Adaptive Big Data Cyber-attack Detection via Semantic Link Networks
Permanent Link
http://hdl.handle.net/11603/11349Metadata
Show full item recordDate
2016-07Type of Work
5 pagesText
conference paper pre-print
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.Rights
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 DetectionSemantic Link Network
Big Data Platform
Streaming Data Analysis
High Performance Computing Facilty (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.