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Intrusion Alert Correlation Based on UFP-Growth & Genetic Algorithm


Anand Jawdekar, Vineet Richariya


Vol. 15  No. 9  pp. 50-53


Intrusion alert correlation is an important factor for network security assessment. In the current scenario various security assessment algorithm are available for risk calculation. These algorithms were qualitative in nature. It is difficult for security managers to configure security mechanisms. The paper discuss the problem of managing alerts. A novel approach for intrusion alert correlation using UFP-Growth and Genetic Algorithm is presented in this paper. UFP-Growth is used for association rule mining and genetic algorithm is used for finding optimal pattern. The proposed method implement in MATLAB 7.8.0. For implement purpose various function and script file were written for implementation of model. For the test of our hybrid method, we used DARPA KDDCUP99 dataset. Our proposed method compare with existing ACR (assessment of credibility and risk) technique and getting better result such as risk calculation and minimized alert correlation rate.


Alert correlation rate, Intrusion alert correlation, Kdd, risk calculation, etc.