Algorithm Optimization and Implementation of a Multi-Layer Network Intrusion Detection System

This paper designs and implements a multi-layer network intrusion detection system (NIDS) using a layered architecture that includes three core functional layers: data collection and preprocessing, feature engineering and detection, and analysis and response. At the algorithm level, an improved incremental SVM algorithm is proposed, along with a hybrid deep learning model based on CNN-LSTM, and an optimized method for determining the density threshold in anomaly detection algorithms. By introducing a dynamic weighting-based multi-algorithm fusion mechanism, the system effectively enhances its detection capabilities against various network attacks. The system is deployed using Docker containerization, integrating core functional modules for distributed data collection, real-time feature extraction, and parallel multi-model detection, achieving high performance and low latency in intrusion detection. Experimental results show that the system meets expected goals in detection accuracy, real-time performance, and scalability, providing effective technical support for security protection in practical network environments.