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Intrusion Alert Correlation based on UFP-Growth and Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 10 |
| Year of Publication: 2012 |
| Authors: Anand Jawdekar, Vineet Richariya |
10.5120/9148-3393
|
Anand Jawdekar, Vineet Richariya . Intrusion Alert Correlation based on UFP-Growth and Genetic Algorithm. International Journal of Computer Applications. 57, 10 ( November 2012), 4-8. DOI=10.5120/9148-3393
Abstract
Intrusion alert correlation is subject to assessment of security and risk level of quantitative analysis of security threats. Intrusion alerts correlation, especially the quantitative characterization of network security and the approach of the build and update of network security scenario and measurement, is one of the important basic approaches of building security services based on the correlation. Various author proposed a model for security analysis of intrusion alert correlation such as Assessment of Credibility, Risk and the Loss of system (ACRL). In this method the correlation value of intrusion find the way of credibility and risk. Some another approach are also used such as graph theory approach for the analysis of node behavior in attack scenario. In this paper we proposed a new algorithm for intrusion alert correlation based on uncertain FP-growth and genetic algorithm. Uncertain FP-growth finds the possibility of probability in attacks occurred before events and mange by the security policy manger. In the process of correlation various value of quantitative are generated some value are exactly correlated and some are low value of quantitative. For the measurement of low value of risk correlation we used genetic algorithm for the optimization process of risk level.
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