ČERMÁK, Milan and Denisa ŠRÁMKOVÁ. GRANEF: Utilization of a Graph Database for Network Forensics. Online. In Proceedings of the 18th International Conference on Security and Cryptography. Portugal: SCITEPRESS, 2021, p. 785-790. ISBN 978-989-758-524-1. Available from: https://dx.doi.org/10.5220/0010581807850790.
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Basic information
Original name GRANEF: Utilization of a Graph Database for Network Forensics
Authors ČERMÁK, Milan and Denisa ŠRÁMKOVÁ.
Edition Portugal, Proceedings of the 18th International Conference on Security and Cryptography, p. 785-790, 6 pp. 2021.
Publisher SCITEPRESS
Other information
Original language English
Type of outcome Proceedings paper
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
Organization Ústav výpočetní techniky – Repository – Repository
ISBN 978-989-758-524-1
ISSN 2184-7711
Doi http://dx.doi.org/10.5220/0010581807850790
UT WoS 000720102500082
Keywords in English Network Forensics;Graph Database;Dgraph;Zeek;Association-based Analysis
Links 833418, interní kód Repo.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 29/4/2022 03:08.
Abstract
Understanding the information in captured network traffic, extracting the necessary data, and performing incident investigations are principal tasks of network forensics. The analysis of such data is typically performed by tools allowing manual browsing, filtering, and aggregation or tools based on statistical analyses and visualizations facilitating data comprehension. However, the human brain is used to perceiving the data in associations, which these tools can provide only in a limited form. We introduce a GRANEF toolkit that demonstrates a new approach to exploratory network data analysis based on associations stored in a graph database. In this article, we describe data transformation principles, utilization of a scalable graph database, and data analysis techniques. We then discuss and evaluate our proposed approach using a realistic dataset. Although we are at the beginning of our research, the current results show the great potential of association-based analysis.
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