Network traffic refers to the amount of data being sent and received over the Internet or any system that connects computers. Analyzing network traffic is vital for security and management, yet remains challenging due to the heterogeneity of plain-text packet headers and encrypted payloads. To capture the latent semantics of traffic, recent studies have adopted Transformer-based pretraining techniques to learn network representations from massive traffic data. However, these methods pre-train on data-driven tasks but overlook network knowledge, such as masking partial digits of the indivisible network port numbers for prediction, thereby limiting semantic understanding. In addition, they struggle to extend classification to new classes during fine-tuning due to the distribution shift. Motivated by these limitations, we propose \Lens, a unified knowledge-guided foundation model for both network traffic classification and generation. In pretraining, we propose a Knowledge-Guided Mask Span Prediction method with textual context for learning knowledge-enriched representations. For extending to new classes in finetuning, we reframe the traffic classification as a closed-ended generation task and introduce context-aware finetuning to adapt to the distribution shift. Evaluation results across various benchmark datasets demonstrate that the proposed Lens~achieves superior performance on both classification and generation tasks. For traffic classification, Lens~outperforms competitive baselines substantially on 8 out of 12 tasks with an average accuracy of \textbf{96.33\%} and extends to novel classes with significantly better performance. For traffic generation, Lens~generates better high-fidelity network traffic for network simulation, gaining up to \textbf{30.46\%} and \textbf{33.3\%} better accuracy and F1 in fuzzing tests. We will open-source the code upon publication.
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation
Pohang University of Science and Technology（浦项科技大学）；School of Electrical Engineering, Pohang University of Science and Technology（浦项科技大学电气工程学院）；NAVER LABS（NAVER实验室）
A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction
Shandong University（山东大学）；Southern University of Science and Technology（南方科技大学）；Northwest University（西北大学）；Didi Chuxing（滴滴出行）；Renmin University of China（中国人民大学）；Jilin University（吉林大学）
the State Key Laboratory of Complex and Critical Software Environment, Beihang University（北京航空航天大学复杂关键软件环境国家重点实验室）；School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通科学与工程学院）
EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs
Monash University（莫纳什大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Chinese Academy of Sciences（中国科学院）；Zhejiang Dahua Technology（浙江大华技术股份有限公司）；Southeast University（东南大学）
PMA-Diffusion: A Physics-guided Mask-Aware Diffusion Framework for TSE from Sparse Observations
Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, 500 Pillsbury Dr. SE, Minneapolis, MN 55455, USA（土木、环境与地球工程系，明尼苏达大学）；Univ. Gustave Eiffel, ENTPE, EMob-Lab, Lyon, F-69675, France（格勒诺布尔伊夫·欧贝尔大学，ENTPE，EMob-Lab，法国里昂）
A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control
School of Artificial Intelligence, Beijing University of Posts（人工智能学院，北京邮电大学）；Beijing Big Data Center（北京大数据中心）；Eastern Institute of Technology（东部技术研究所）
Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling
School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；KDDI Research（KDDI研究所）
PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation
University of Electronic Science and Technology of China（电子科技大学）；University of Hong Kong（香港大学）；Renmin University of China（中国人民大学）；Hong Kong Polytechnic University（香港理工大学）
LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data
Harbin Institute of Technology（哈尔滨工业大学）；University of Iowa（爱荷华大学）；Northern Illinois University（北伊利诺伊大学）；Massachusetts Institute of Technology（麻省理工学院）；Logistics and Supply Chain MultiTech R&D Centre（物流及供应链多元技术研发中心）
Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge
Chinese Academy of Sciences, China（中国科学院）；Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA（麻省理工学院城市研究与规划系）；School of Vehicle and Mobility, Tsinghua University, China（清华大学车辆与移动系统学院）
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
School of Cyber Science and Technology, Beihang University（北航信息科学与技术学院）；Hangzhou Innovation Institute of BUAA, Hangzhou, China（北京航空航天大学杭州创新研究院）；Department of Computer Science and Technology, Shantou University（汕头大学计算机科学与技术学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Department of Computing, Macquarie University（麦考瑞大学计算机学院）；National University of Defense Technology, Changsha, China（国防科技大学）
