ResLearn-XR is a residual learning framework designed to predict extended reality (XR) network traffic and estimate Quality-of-Experience (QoE) risk. It uses a two‑stage temporal learning structure: a base sequence prediction model followed by task‑specific residual components that operate in value space for traffic forecasting and in logit space for probabilistic QoE risk estimation. The framework introduces a Data Descriptor Algorithm (DDA) to convert packet‑level observables into frame‑timing‑aware descriptors and is evaluated on a newly constructed XR Traffic‑QoE dataset, achieving significant reductions in SMAPE for both traffic prediction and QoE‑risk estimation compared to single‑stage baselines.
By Yoga Suhas Kuruba Manjunath, Jie Gao, Lian Zhao
QoNext is a new framework that applies Quality of Experience (QoE) principles from networking and multimedia to evaluate foundation models in a conversational setting. It identifies experiential factors that influence user experience, collects human ratings in controlled interaction scenarios, and builds a database and neural predictor to estimate user satisfaction from system parameters. The framework demonstrates the ability to decode the mechanisms of user satisfaction and predict human sentiment across varied service conditions.
By Yijin Guo, Farong Wen, Ye Shen, Junying Wang, Qi Jia, Xiaohong Liu, Zicheng Zhang, Guangtao Zhai
arXiv:2402.03694v3 Announce Type: replace-cross
Abstract: Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encryp...
By Shinan Liu, Ted Shaowang, Gerry Wan, Jeewon Chae, Jonatas Marques, Sanjay Krishnan, Nick Feamster
arXiv:2510. 00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via real-time video communication (RTC), just like chatting with real people.
By Jiayang Xu, Xiangjie Huang, Zijie Li, Antariksh Verma, Zili Meng
The paper evaluates seven pre‑trained CNN architectures for detecting IoT DDoS attacks under resource constraints, using the CICDDoS2019 dataset and image‑based traffic representation. It combines performance metrics, reliability statistics, latency and training cost, and interpretability methods (Grad‑CAM and SHAP) to assess each model. DenseNet169 and MobileNetV3 emerge as top performers, offering strong reliability, interpretability, and a favorable latency‑accuracy trade‑off for fog‑level deployment.
By Nelly Elsayed
arXiv:2607. 22854v1 Announce Type: new Abstract: AI agents are enabling a new paradigm of agent-augmented real-time communication (RTC), where humans focus on high-level collaboration, while agents autonomously retrieve, analyze, and generate information in real time to support their interactions.
By Goodsol Lee, Juheon Yi, Jinglu Wang, Haowen Xu, Saewoong Bahk, Yan Lu
The paper demonstrates that encrypted traffic classification accuracy can be dramatically inflated by the choice of data split protocol. Using the same model and code, macro‑F1 scores drop from 0.992 under a standard random split to 0.567 when flows from a single packet capture are kept on one side of the split, a gap of 0.426 that is statistically significant. The authors evaluate multiple split strategies on QUIC traffic, showing that server‑disjoint splitting harms performance while other splits are comparable, and provide a diagnostic based on single‑identifier probes to identify the appropriate split axis before training.
By Mayan Sharma, Kriti Saini, Devansh Behl
arXiv:2608.30745v1 Announce Type: new
Abstract: The widespread adoption of encrypted traffic poses severe challenges to current security situational awareness systems based on network traffic monitor...
By Ze Chen, Qiming Yu, Zijia Song, Guozheng Yang, Wei Yan
arXiv:2606. 04517v1 Announce Type: cross Abstract: Graph-based deep learning methods have been widely employed in encrypted traffic analysis to exploit latent correlations across different granularities.
By Yuantu Luo, Jun Tao, Linxiao Yu, Guang Cheng
arXiv:2603.25507v2 Announce Type: replace-cross
Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited...
By Giampaolo Bovenzi, Domenico Ciuonzo, Jonatan Krolikowski, Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e, Dario Rossi
The paper introduces a semantic‑aware multi‑level neural video codec designed for low‑latency, task‑oriented video transmission over unreliable channels. It builds on the real‑time DCVC‑RT codec by partitioning encoded representations into packets of varying semantic and feature importance, assigning them to priority streams, and employing an error‑resilient entropy model that removes inter‑packet dependencies. Experiments demonstrate that this framework improves robustness against packet erasures, achieving graceful degradation in less important regions while preserving task‑relevant visual content.
By Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du, Harish Viswanathan
arXiv:2608.29331v1 Announce Type: new
Abstract: Video traffic constitutes a significant share of global web traffic. To reduce its volume, video codecs have been developed and continuously improved....
By Nikolay Safonov, Nikita Gornostaev, Alexandra Dubonos, Dmitriy Vatolin