Data Mining in Long-Term Honeypot Data
作者： Daniel Fraunholz , Marc Zimmermann , Alexander Hafner , Hans D. Schotten
关键词:
摘要: Criminal activity in the Internet is becoming more sophisticated. Traditional information security techniques hardly cope with recent trends. Honeypots proved to be a valuable source of threat intelligence. In this work several are combined into Honeynet and observed exploitation attempts. The consists six was operated for 222 days. 12 million attempts were captured. captured data examined evaluated. Several hypotheses proposed analyzed. Dependencies distribution within identified quantified. Investigated features are: Temporal spatial distribution, attacked protocols, involved autonomous systems employed dictionaries.
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