Computer security plays an important role in everybody's life. Therefore, to protect the computer and sensitive information from the untrusted parties have great significance. Intrusion detection system helps us to detect these malicious activities and sends the reports to the administration. But there is a problem of high false positive rate and low false negative rate. To eliminate these problems, hybrid system is proposed which is divided into two main parts. First, cluster the data using K-Mean algorithm and second, is to classify the train data using Adaptive-SVM algorithm. The experiments is carried out to evaluate the performance of proposed system is on NSL-KDD dataset. The results of proposed system clearly give better accuracy and low false positive rule and high false negative rate.

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Software Defined Networking is an adaptable way of networking, which disconnects data forwarding plane and control-plane of system equipment’s and also solves issues in existing network infrastructure. More specifically, the control-plane of software defined network decides the advancing way of network flow with Centralized Control Manner (CCM). SDN (Software Defined Networking) is a strategy for making, planning and overseeing systems which intend to change this present unfortunate circumstance. It has been used in dissimilar areas, like a campus networks and data center systems. In this survey paper, we’ve reviewed the concept of (SDNs) Software Defined Networks, its architecture and applications. In the survey, it has been found that SDN load balancing has become more smart and efficient and reduces the statistic collection overhead and maintain better QoS (Quality of Service) data rates. In addition, we reviewed the direct routing based algorithms of Load Balancer and compare with Round Robin Strategy. Furthermore, we’ve reviewed and compared the existing work to get better idea about the concept of Load balancing.

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