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An Efficient Intrusion Detection System Based on GA to Recognize Attacks in User Privileges
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Abstract
Network Security is an important issue, almost 70% of data security threats are created within the organization. For all the organizations when they share the information from one to another place the threat may occur. Presently the data can be shared on any network are always being a risk of intrusion of attacks. The finding of attacks by using Intrusion Detection System (IDS) against computer networks is becoming a most important problem to resolve in the area of network security. There are various approaches are being utilized for security purpose. In this paper, a genetic algorithm is proposed to identify various detection of intrusion/harmful attack from unauthorized user (External attacks) in addition to attacks by authorized users (Internal attacks) based on the privileges given to them. The algorithm provides many features when the information in such a protocol type like Time duration, security, maintenance in categorization on rule set. The rule set is valid up to specific type of attacks. The training dataset are used to create the set of rules to recognize the type of attacks in the networks using fitness function. The dataset training has completed on the KDD99 datasets and Network overflow is taken which will reduce the complexity of security.
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