Abstract:
Predicting network traffic is essential to optimize network resource management, congestion avoidance, anomaly detection, and QoS in general. This paper presents and investigates a deep learning technique for the precise forecast of future network traffic through historical data. A dataset of 1,000,000 records and 11 features was used (700,000 samples for training and 300,000 for testing). The raw traffic data was divided into a normal class and an anomaly class for the classification and anomaly detection task. Data Preprocessing The steps of missing value treatment, normalization, data cleaning, and reshaping the data for meeting the minimum input requirement of the deep learning model were performed. Three deep learning algorithms (multilayer perceptron (MLP), feedforward neural network (FNN), and autoencoder (AE)) were developed and evaluated. They were chosen for their ability to model complex, non-linear relations from the network traffic and in order to obtain representations that traditional statistical models have not been able to learn. The experimental results showed that the MLP and FNN models produced high accuracy rates of 0.99, which was indicative of high predictive ability. The Autoencoder, despite its inferior performance with an accuracy of 0.94, also performed well in unsupervised learning and anomaly detection. Performance measures such as precision, recall, F1-score, confusion matrices, and ROC/Precision-Recall curves demonstrated the robustness and generalization of the models. The comparative study demonstrated that deep learning models, such as MLP and FNN, were more efficient than the conventional statistical predicting anomalies models. These results demonstrate the efficiency and scalability of deep learning for real-time network traffic prediction and anomaly detection, providing an intelligent and proactive network management technique for advanced communication systems. We deployed our top-performing model online and are currently examining the results produced visually
