In this system the intrusion detection is one of the major research problems in network security. This is the process of monitoring and analyzing network traffic data to detect security violations. In this paper, we present the experimental results in our project to evaluate the different performance like (e.g., IDS, Malware, etc.). We analyze some different algorithms with dimensionality reduction and classification algorithm with the dataset that is constructed from the KDD CUP dataset. Data

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... ining approach can also play a very important role in developing an intrusion and detection technique. The network traffic can be classified into normal and anomalous in order to detect intrusion detection. In our work, we use five (5) different algorithm's namely logistics regression, decision tree, random forest, KNN, KernelSVM are we used in the classification algorithm. The comparison of this classification algorithm is presented in this paper based upon their accuracy, timing, and performance to find out suitable algorithm's available and this method are performed in the spyder tool using UNSW-NB15 dataset.