<![CDATA[The tech-savvy generation of today uses network, which has become embedded in everyone’s routine and is inseparable from our life. Hence securing your network from various network attacks becomes paramount for us to keep our sensitive data online safe. One of the most noticeable threats to the security of networks is intruder. Due to the number of challenges faced by the Intrusion Detection System, it is a priority to detect malicious activities in a network and have an enhanced performance to cope with the increasing network traffic. The results of our experiment throw a light of our IDS ability to improve the attack detection rate and decreasing the FAR. This Intrusion detection system can do with a low FAR and the low FAR important for its functioning. The time required to train to test the model is significantly reduced by feature selection and implementing the layered approach with the help of GA and RBF. We address two concepts in this paper, first deals with features reduction based on correlation and secondly genetic algorithm rules formation with RBF training. The overall accuracy and efficiency of the system is improved by integrating them.]]>
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