Intrusion Detection System in A Hybrid Network using Data Mining-Resistance System

Data mining methods can be very useful to add to the Intrusion Detection System(IDS) to identify common and suspicious activity patterns and help managers protect sensitive data and all information contained in busin esses or organizations. This paper attempts to analyze the types of network attacks and the existing IDS drifts based on Data Mining techniques. In recent days, the growth of science, information technology, and communications, in particular, has resulted in a rise in the quantity of digital data. These enormous quantities of information are no longer able to handle standard (statistical) techniques. Data Mining ( DM) has emerged and has proved to be one of the most successful alternatives for analyzing large quantities of data . Data Mining has been developed with the assistance of many fields, including Statistics, Databases, and Artificial Intelligence. This paper focuses on tracking IDS in a hybrid network using the Genetic Algorithm (GA), by applying essential functions of IDS technologies, standard detection methodologies as signaturebased and anomaly-based methods of detection.