The paper investigates using Convolutional Neural Networks (CNNs) for deanonymizing I2P traffic patterns, but concludes that the proposed methods do not compromise the network's anonymity guarantees.

This paper proposes a distributed architecture for automating and controlling multi-vendor, multi-layer IP over DWDM networks using SDN, enabling end-to-end service lifecycle automation and closed-loo…

This paper surveys Neural Architecture Search methods for traffic prediction, automating the design of deep learning models for spatial-temporal traffic data.

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

This paper proposes Autogenic network management, a self-programming extension to agentic AI for next-generation network management in 6G networks.

A novel multi-agent reinforcement learning algorithm, TC-GQN, is proposed for high-level control and orchestration of mobile networks, enabling energy savings while maintaining QoS.

This paper proposes a decentralized Multi-Agent Reinforcement Learning framework with a Graph Neural Network for connected and autonomous vehicles to reduce traffic shockwaves.

The paper proposes Predictive Routing Replay (PR2) to stabilize reinforcement learning on Mixture of Experts (MoE) LLMs by predicting and incorporating short-horizon router evolution during training a…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

This paper presents AlphaRoute, a multi-objective adaptive search framework for VLSI global routing using Large Language Models as semantic policy optimizers.

MambaNetBurst introduces a compact, tokenizer-free byte-level classifier using a Mamba-2 backbone to achieve strong network traffic classification without requiring pre-training or complex data prepro…

AlphaTransit introduces a novel search-based planning framework that combines Monte Carlo Tree Search (MCTS) with a neural policy-value network to efficiently design high-quality, city-scale bus trans…

This paper explores the application of explainability techniques to Reinforcement Learning algorithms in Air Traffic Control using a simplified environment and a saliency map.

This paper demonstrates that autonomous aircraft can self-organize into efficient corridor flows in decentralized settings.

This paper proposes a self-adaptive channel assignment framework using Q-learning to optimize system throughput, user fairness, and interference mitigation.

The paper develops a trust-aware framework to model how connected vehicles adapt their routing decisions and overall traffic flow when exposed to misinformation, showing that endogenous trust provides…

The paper introduces CCLab, an adversarial testing framework, to systematically evaluate the robustness of both learning-based and traditional congestion controllers, finding that learning-based contr…