Development of Intrusion Detection Using Logistic Regression with Various Preprocessing Approaches

Preprocessing is very important to predict Intrusion Detection System (IDS) with respect to any parameters. It entails prepping and converting raw data into a format compatible with Machine Learning (ML) algorithms. ML approaches are used to categorize network activity as either legitimate or malicious to create IDS. For binary classification problems like intrusion detection, one such approach is LR (Logistic Regression). The data must be preprocessed for modeling to be effective. In the present investigation, Min-Max Normalization, SMOTE for controlling class imbalance, and Z-score Normalization were used in conjunction with PCA feature extraction and LR (Logistic Regression) for classification of intrusions. It is possible to considerably increase the accuracy, f1-score, precision, and recall of the IDS by combining the preprocessing method Z-score Normalization for normalization with PCA feature extraction. From the results obtained proposed Z Score + LR produces Accuracy of 88.3%, Precision of 0.86, Recall of 0.84 and F Measure of 0.8. The tool used is Jupyter Notebook and language used is python.