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Design of Intrusion Detection Model Based on FP-Growth and Dynamic Rule Generation with Clustering

Author(s): Manish Somani | Roshni Dubey

Journal: International Journal of Advanced Computer Research
ISSN 2249-7277

Volume: 3;
Issue: 10;
Start page: 146;
Date: 2013;
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Keywords: ARM | K - Means | DR | FPR | FNR

ntrusion Detection is the process used to identifyintrusions. If we think of the current scenario thenseveralnew intrusion that cannot be prevented bythe previous algorithm, IDS is introduced to detectpossible violations of a security policy by monitoringsystem activities and response in all times forbetterment. If we detect the attack type in aparticular communication environment, a responsecan be initiated to prevent or minimize the damageto the system. So it is a crucial concern. In ourframework we present an efficient framework forintrusion detection which is based on AssociationRule Mining (ARM) and K-Means Clustering. K-Means clustering is use for separation of similarelements and after that association rule mining isused for better detection. Detection Rate (DR), FalsePositive Rate (FPR) and False Negative Rate (FNR)are used to measure performance and analysisexperimental results
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