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Efficient Extraction of Network Event Types from NetFlows
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-06
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
To perform sophisticated traffic analysis, such as intrusion detection, network monitoring tools firstly need to extract higher-level information from lower-level data by reconstructing events and activities from as primitive information as individual network packets or traffic flows.
Aggregating communication data into meaningful entities is an open problem and existing, typically clustering-based, solutions are often highly suboptimal, producing results that may misinterpret the extracted information and consequently miss many network events.
We propose a novel method for the extraction of various predefined types of network events from raw network flow data.
The new method is based on analysis of computational properties of the event types as prescribed by their attributes in a given descriptive language.
The corresponding events are then extracted with a supreme recall as compared to a respective event extraction part of an in-production intrusion detection system Camnep.
American Psychological Association (APA)
Sourek, Gustav& Zelezny, Filip. 2019. Efficient Extraction of Network Event Types from NetFlows. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1210634
Modern Language Association (MLA)
Sourek, Gustav& Zelezny, Filip. Efficient Extraction of Network Event Types from NetFlows. Security and Communication Networks No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1210634
American Medical Association (AMA)
Sourek, Gustav& Zelezny, Filip. Efficient Extraction of Network Event Types from NetFlows. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1210634
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210634