Research on Intelligent Network Security Event Detection and Emergency Disposal Technology

Abstract This article studies intelligent network security event detection and emergency response technology. In response to the severe challenges faced by the current network security field, a CNN-LSTM hybrid model CNN-LSTM_HMNID (CNN-LSTM Hybrid Model for Network Intrusion Detection) combined with deep learning is proposed for network intrusion detection. This model automatically extracts local features of network traffic data through Convolutional Neural Networks (CNN), and uses Long Short Term Memory Networks (LSTM) to capture the temporal dependencies between these features, achieving accurate detection of abnormal network behavior. Studies indicate that the CNN-LSTM_HMNID architecture effectively recognizes diverse attack patterns, including Distributed Denial of Service (DDoS) intrusions, port sweeps, and deceptive phishing attempts. It achieves superior detection rates when benchmarked against traditional classifiers such as Support Vector Machines (SVM) and the Random Forest approach. Furthermore, the paper presents insights into the development and deployment of an intelligent emergency response mechanism. This system can rapidly pinpoint the origin of malicious activities and enhance both the speed and precision of crisis management by automating the analysis of security breach characteristics and behaviors. The research contributes innovative concepts and strategies to the realm of intelligent cyber defense and offers a robust foundation for the establishment of a more secure online ecosystem.