CLASSIFICATION OF NETWORK INTRUSION DETECTION SYSTEM USING RECURRENT NEURAL NETWORKS

A Network Intrusion Detection System (NIDS) is a key technology in network security that detects packets of malicious or unwanted abnormal activity occurring in the network. These network intrusion detection systems have been studied together with machine learning and deep learning, but performance is not guaranteed in the actual environment, or the class balance problem has not been solved. In existing system, the performance of a discretization pre-processing method with a CNN-based classifier on the class imbalance problem of network traffic data has been investigated. The pre-processing method adds a discretization algorithm for continuous variables is the commonly used conventional pre-processing method. In proposed system, the project is expected to show better results by implementing Recurrent Neural Networks (RNN)