Research on network security situation analysis based on wavelet transform and support vector machine

This paper proposes a novel approach to analyzing network security by integrating wavelet transform and Support Vector Machine (SVM) techniques. With the increasing complexity of network environments and the prevalence of cyberattacks, traditional security analysis methods struggle to accurately model the nonlinear and dynamic nature of network traffic. The proposed method employs wavelet decomposition to extract multi-scale features from network traffic and utilizes SVM for robust pattern recognition and predictive modeling. Experimental results based on real-world network traffic demonstrate that the proposed model significantly improves prediction accuracy and reduces the rates of common attack types, such as Denial-of-Service (DoS) and unauthorized access attacks. Additionally, the model’s efficiency in training and adaptability to diverse attack patterns make it a promising solution for modern network security challenges.