Network traffic monitoring based on CNN-SVM - Wu Qian (ITM Web of Conferences)
In a modern complex network, network monitoring and measurement have become increasingly important. The traditional network traffic monitoring methods face the challenge of efficiency and accuracy when dealing with massive data. The proposed hybrid model in this study uses convolutional neural networks (CNNs) and support vector machines (SVMs) to address these concerns and increase the effectiveness of network traffic monitoring. This paper uses CNN to extract features from network traffic data. CNN has the ability to recognize intricate patterns in the data and automatically extract valuable characteristics from the raw data. The SVM classifier receives the retrieved characteristics and uses them to further classify the data in order to distinguish between normal and abnormal traffic. By doing this, this paper may more successfully combine the benefits of SVM for classification with CNN’s advantages for feature learning, enhancing traffic monitoring’s precision and resilience. According to the experimental data, the hybrid model performs far better in network traffic categorization tasks than the standard techniques, with a reduced false positive rate and higher accuracy. This research shows that CNN-SVM model is an effective network traffic monitoring tool, which can provide high quality detection results while ensuring high efficiency.