A Novel Anomaly Detection System Based on HFR-MLR Method

Reducing the data space and then classifying anomalies based on the reduced feature space is vital to real-time intrusion detection. In this study, a novel framework is developed for logistic regression-based anomaly detection and hierarchical feature reduction (HFR) to preprocess network traffic data before detection model training. The proposed dimensionality reduction algorithm optimally excludes the redundancy of features by considering the similarity of feature responses through a clustering analysis based on the feature space reduced by factor analysis, thus helping to rank the importance of input features (essential, secondary and insignificant) with low time complexity. Classification of anomalies over the reduced feature space is based on a multinomial logistic regression (MLR) model to detect multi-category attacks as an outcome with the goal of reinforcing detection efficiency. The proposed system not only achieves a significant detection performance, but also enables fast detection of multi-category attacks.