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Statistical Analysis of Unauthorized-Access Log Data and Its Interpretation

Minami, Hiroyuki


We have studied how we analyse unauthorized network access logs and our empirical studies have suggested that we could classify the logs into some typical patterns and tried to develop methodology, to reveal them with aggregated statistical methodologies including symbolic data analysis (SDA). Our motivation is to extract some specific patterns from the logs. Many applications have been already developed to detect anomalies from them, but few are mainly based on statistics. To improve their quality, a mathematical viewpoint is key since most unauthorized actions are based on automatic algorithms. Thus we could apply some statistical (and intensive) model to them. When we develop an intensive statistical analysis for this data, SDA, known as a typical aggregated data analysis method would be applicable. In the study, we discuss how we aggregate the original log data and derive a reasonable classification and interpretation through the analyses.

Verlagsausgabe §
DOI: 10.5445/KSP/1000098011/13
Veröffentlicht am 13.10.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000138944
Erschienen in Archives of Data Science, Series A
Band 6
Heft 1
Seiten P13, 10 S. online
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