Multi-Feature Wavelet-Based Network Intrusion Detection Using Machine Learning and Data Balancing Techniques

Abstract

Modern digital infrastructures such as enterprise networks, cloud systems, and IoT environments generate large volumes of dynamic network traffic, including packet data, authentication logs, and system metrics. Real-time monitoring of this data is critical for ensuring network security and preventing unauthorized access. However, traditional intrusion detection methods rely on rule-based mechanisms and manual analysis, which are often inefficient, inflexible, and unable to adapt to evolving cyber threats. These limitations result in delayed detection, high false positive rates, and poor scalability in complex environments. To overcome these challenges, a multi-feature wavelet-based network intrusion detection framework is proposed using machine learning and data balancing techniques. The system incorporates key network features such as packet size, latency, bandwidth utilization, and authentication metrics. Additionally, Discrete Wavelet Transform (DWT)-based features are used to improve signal representation and uncover hidden patterns in network behavior. Machine learning models including K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Naive Bayes are applied for both binary anomaly detection and multi-class classification of authentication failures. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is employed, enhancing model performance and reducing bias. Feature scaling through standardization ensures consistency across models. The framework is implemented using a modular pipeline and deployed via a Flask-based web interface for real-time prediction. Performance evaluation using accuracy, precision, recall, and F1-score demonstrates effective detection of anomalies and reliable classification, making the system scalable and suitable for modern network security applications.