XInternet (eXplainable Internet)

Exciting news! Our recent paper, "Cross-Network Embeddings Transfer for Traffic Analysis," featured in IEEE Transactions on Network and Service Management, introduces innovative approaches to enhance traffic analysis using artificial intelligence (AI). We address the challenge of limited labeled datasets and dynamic networking environments by proposing techniques to transfer knowledge between different networks. By aligning embeddings and leveraging transfer learning methods, our research facilitates the adoption of AI-based solutions for traffic analysis and cybersecurity. Through comprehensive experimental analysis, we demonstrate the effectiveness of our approach in both supervised and unsupervised tasks related to darknet and honeypot traffic. This work paves the way for collaborative knowledge sharing between network providers and customers, leading to improved network intelligence and management. Explore our paper for insights into advancing network analytics through cross-network knowledge transfer

Exciting news! Our latest research, "Explainable Deep-Learning Approaches for Packet-Level Traffic Prediction of Collaboration and Communication Mobile Apps," has been published in the prestigious IEEE Open Journal of the Communications Society. Although it's currently in early access, this study sheds light on the application of explainable artificial intelligence (XAI) in predicting packet-level traffic generated by popular communication apps like Skype, Teams, Webex, and Zoom. Dive into the paper to uncover how XAI improves the interpretability and reliability of traffic predictions, offering invaluable insights for network management tasks. Access the early release to stay ahead in understanding and optimizing network infrastructures.