TriNet: A Synergistic Hybrid Approach for Network Traffic Anomaly Detection

Network traffic anomaly detection is essential for securing digital infrastructures, yet traditional methods often fail to handle the complexity and dynamics of network data effectively. Additionally, this paper presents a hybrid model called TriNet, which consists of convolutional neural networks (CNNNs), long short-term memory (Bi-LSTM) networks, and attention mechanisms integrated into one cohesive system. This allows intricate spatiotemporal patterns in network traffic to be captured in a synergistic way. Our model employs CNNs for extracting spatial features, Bi-LSTMs for modeling temporal sequences, and Attention mechanisms for focusing on critical features, augmented by residual connections to enhance training dynamics. Based on experimental findings conducted on the KDD Cup 1999 dataset, the TriNet model has shown significant improvements in terms of accuracy, precision, recall, as well as the F1 score versus traditional machine learning models. TriNet's innovative architecture and robust performance underscore its potential as a powerful tool for network traffic anomaly detection, offering substantial improvements in network security.