Published Paper Details:

The evolving nature of network infrastructures and increasing complexity in cyber attacks have deemed the need for more intelligent analysis processes from networks. The conventional rule-based and statistical anomaly detection approaches are typically outperformed by new threats, as they are based on signature match (static) validation. In this article, we will analyze how deep learning techniques are integrated into the real-time anomaly detection and optimization of network traffic analysis. Improved precision and flexibility of identifying malicious activities from the models encompassed in AI driven models like CNNs (convolutional neural networks), RNNs(recurrent neural networks) and autoencoders. Beyond that, reinforcement learning and predictive analytics are pivotal in optimising network traffic load balancing, congestion control, Quality-of-Service, (QoS) as well. Real-World Case Studies This study enumerates the major force at play, computational overhead and adversarial attacks as well as future direction implications (edge AI, XAI for cybersecurity) combining with AI Powered (network) traffic Analytics organizations are able to establish a stronger, scalable and responsive security framework to be more secure from contemporary cyber threats.