An AI System for Network Anomaly Traffic Detection Integrating Convolutional Neural Networks and Long Short-Term Memory Networks

As modern networks grow in complexity and size, the task of identifying abnormal traffic patterns has become increasingly difficult yet essential. This research proposes an AI-based anomaly detection system that leverages the complementary capabilities of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models to effectively identify abnormal and potentially harmful activities within network traffic. The CNN aspect focuses on deriving advanced spatial characteristics from unprocessed network data, whereas the LSTM element is designed to recognize time-based relationships and sequence trends. Dubbed Convolutional and LSTM-based Anomaly Detector (CLAD), this combined structure allows the system to attain enhanced accuracy and reliability in detection, surpassing conventional machine learning techniques. Tests conducted on standard datasets reveal that our approach excels in accuracy, sensitivity, and timely detection performance, presenting an effective tool for advanced and preemptive network security oversight.