HUSÁK, Martin, Jaroslav KAŠPAR, Elias BOU-HARB and Pavel ČELEDA. On the Sequential Pattern and Rule Mining in the Analysis of Cyber Security Alerts. In Proceedings of the 12th International Conference on Availability, Reliability and Security. Reggio Calabria: ACM. p. "22:1"-"22:10", 10 pp. ISBN 978-1-4503-5257-4. 2017.
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Basic information
Original name On the Sequential Pattern and Rule Mining in the Analysis of Cyber Security Alerts
Authors HUSÁK, Martin (203 Czech Republic, guarantor, belonging to the institution), Jaroslav KAŠPAR (203 Czech Republic, belonging to the institution), Elias BOU-HARB (124 Canada) and Pavel ČELEDA (203 Czech Republic, belonging to the institution).
Edition Reggio Calabria, Proceedings of the 12th International Conference on Availability, Reliability and Security, p. "22:1"-"22:10", 10 pp. 2017.
Publisher ACM
Other information
Original language English
Type of outcome Proceedings paper
Field of Study Informatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/00216224:14610/17:00094473
Organization Ústav výpočetní techniky – Repository – Repository
ISBN 978-1-4503-5257-4
UT WoS 000426964900022
Keywords in English data mining;cyber security;sequential pattern mining;sequential rule mining;alert correlation;attack prediction
Links VI20162019029, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 4/9/2020 06:31.
Abstract
Data mining is well-known for its ability to extract concealed and indistinct patterns in the data, which is a common task in the field of cyber security. However, data mining is not always used to its full potential among cyber security community. In this paper, we discuss usability of sequential pattern and rule mining, a subset of data mining methods, in an analysis of cyber security alerts. First, we survey the use case of data mining, namely alert correlation and attack prediction. Subsequently, we evaluate sequential pattern and rule mining methods to find the one that is both fast and provides valuable results while dealing with the peculiarities of security alerts. An experiment was performed using the dataset of real alerts from an alert sharing platform. Finally, we present lessons learned from the experiment and a comparison of the selected methods based on their performance and soundness of the results.
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