AI-Driven Anomaly Prediction in Encrypted Network Traffic

The rapid growth of computer networks has increased demand for more sophisticated tools for network traffic analysis and monitoring. The increasing reliance on networks has amplified the need for robust security and intrusion detection mechanisms. Numerous studies have sought to develop efficient methods for fast and accurate intrusion detection, each addressing the challenge from different perspectives. A common limitation among these approaches is their reliance on expert-engineered features extracted from network traffic. This dependency makes them less adaptable to emerging attack techniques and changes in normal traffic patterns, often resulting in suboptimal performance. In this study, we propose a method leveraging recent advancements in artificial neural networks and deep learning, specifically using recurrent neural networks (RNNs), for network traffic analysis and intrusion detection. The key advantage of this approach is its ability to autonomously extract features from network traffic without human intervention. Trained on the ISCX IDS 2012 dataset, the proposed model achieved an accuracy of 0.99 in distinguishing between malicious and normal traffic.