Intrusion detection system using fuzzy genetic algorithm

Computer security has become an important part of the day today's life. Not only single computer systems but an extensive network of the computer system also requires security. In achieving the safety of the systems, an Intrusion Detection System (IDS) plays a significant role. IDS is a software that monitors the computer network and detects the suspicious activities that occur in the systems or network. The process of intrusion detection includes detecting intrusion. Intrusion is a suspicious activity attempted by the attacker. This paper presents a fuzzy-genetic approach to detecting network intrusion. Paper presents the results of the proposed system in terms of accuracy, execution time, and memory allocation. To implement and measure the performance of the system the KDD99 benchmark dataset is used. The KDD99 dataset is a benchmark dataset that researchers use in various network security researches. Genetic algorithm includes a development and collection that uses a chromosome like data structure and develop the chromosomes using selection, crossover and mutation operators. Fuzzy rule sorts network attack data.