Forecast techniques for predicting increase or decrease of attacks using bayesian inference

Chie Ishida, Yutaka Arakawa, Iwao Sasase, Keisuke Takemori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

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, it is necessary to forecast a fluctuation of attacks. However, there is no approach for predicting the fluctuation of attacks, since the fluctuation of attacks seems to be random. In this paper, we propose forecast techniques for predicting increase or decrease of the attacks by using the Bayesian Inference for calculating the conditional probability based on past-observed event, counts. We consider two algorithms by focusing on an attack cycle and a fluctuation range of the event counts. We implement a forecasting system and evaluate it with real IDS events. As a result, our proposed technique can forecast increase or decrease of the event counts, and be effective to various types of attacks.

Original languageEnglish
Title of host publication2005 IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing, PACRIM - Proceedings
Pages450-453
Number of pages4
DOIs
Publication statusPublished - 2005 Dec 1
Event2005 IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing, PACRIM - Victoria, BC, Canada
Duration: 2005 Aug 242005 Aug 26

Publication series

NameIEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings
Volume2005

Other

Other2005 IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing, PACRIM
CountryCanada
CityVictoria, BC
Period05/8/2405/8/26

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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