A detail analysis on intrusion detection datasets

To simulate an efficient Intrusion Detection System (IDS) model, enormous amount of data are required to train and testing the model. To improve the accuracy and efficiency of the model, it is essential to infer the statistical properties from the observable elements of th e dataset. In this work, we have proposed some data preprocessing techniques such as filling the missing values, removing redundant samples, reduce the dimension, selecting most relevant features and finally, normalize the samples. After data preprocessing, we have simulated and tested the dataset by applying various data mining algorithms such as Support Vector Machine (SVM), Decision Tree, K nearest neighbor, K-Mean and Fuzzy C-Mean Clustering which provides better result in less computational time.