Forecast techniques for predicting increase or decrease of attacks using Bayesian inference
作者： C. Ishida , Y. Arakawa , I. Sasase , K. Takemori
DOI: 10.1109/PACRIM.2005.1517323
关键词:
摘要: The analysis techniques of intrusion detection system (IDS) events are actively researched, since it is important to understand attack trends and devise countermeasures against incidents. To aim at a quick response in security operation, necessary forecast fluctuation attacks. However, there no approach for predicting the attacks, attacks seems be random. In this paper, we propose increase or decrease by using Bayesian inference calculating conditional probability based on past-observed event counts. We consider two algorithms focusing an cycle range implement forecasting evaluate with real IDS events. As result, our proposed technique can counts, effective various types
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sci-hub.se 参考文章(4)
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