LIGHTWEIGHT INTRUSION DETECTION METHOD THROUGH CORRELATION BASED HYBRID FEATURE SELECTION

摘要

A lightweight intrusion detection method through correlation based hybrid feature selection in a computer is provided to remarkably reduce a training and testing time while keeping a stable feature detection result, a low false detection rate, and a high detection rate. Preprocessed checking data from checking data is classified into a training dataset and a testing dataset. The training dataset is classified again into a feature selection dataset processing through a hybrid feature selection process based on correlation resulted as a set of selected features, a model building dataset used for constructing an intrusion detection model using the selected feature, and a verification dataset. The intrusion detection model is verified by the verification dataset and is tested by the testing dataset. The correlation based hybrid feature selection process is sent to the modeling dataset having the reduced feature and the verification dataset having the reduced feature through an adder. The modeling dataset and the verification dataset are sent to a classification model through machine learning and verification.