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International Conference on networking and network Applications (NaNA)

作者： Wenjiang Ji Yixin Yang Yaling Zhang Yichuan Wang Mengjie Tian Yuan Qiu School of computer science and engineering Xi'an University of technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology

Distributed Denial of Service(DDoS) attacks target the forwarding-control separation feature of Software-Defined networking(SDN) to launch attacks, causing network disruptions. Therefore security against DDoS attack detection for SDN controllers is the focus of current research. This paper proposes an Extreme Gradient Boosting (XGBoost) DDoS attack detection algorithm based on a combination of information gain and recursive feature elimination algorithms. We evaluated the performance of the method using the CICDDoS2019 dataset. This method works well in multi-classification model for attack detection with an accuracy of 93.41%.

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PFV2: Packet fragmentation with variable size and vigorous mapping in time-sensitive networking

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Journal of Systems Architecture 2025年 166卷

作者： Yan, Wenyan Fu, Bin Wei, Dongsheng Li, Renfa Lei, Yixue Jia, Yuhang Xie, Guoqi Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing401120 China Future Network Laboratory Tencent Beijing100080 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou423000 China

With the rapid advancement of intelligent automobiles, the ever-growing communication data put forward high bandwidth and low latency requirements. To meet these requirements, automotive Original Equipment Manufacturers (OEMs) widely adopt domain-centralized Electrical/Electronic (E/E) architecture. In this architecture, Time-Sensitive networking (TSN) is expected to serve as the backbone network because of its high bandwidth and deterministic communication. TSN uses the Gate Control List (GCL) to divide its time length into multiple time slots, and the time intervals of these time slots are non-uniform (equal or unequal). Different flows have varying requirements for time slot sizes. To enhance the acceptance ratio of Time-Triggered (TT) flows through GCL time slot allocation, packet fragmentation (i.e., flow fragmentation) is introduced into TSN in the recent study. The state-of-the-art packet fragmentation solution divides one un-schedulable TT flow into multiple equal-sized packets (i.e., equal-sized time slots). In other words, these fixed-size packets are difficult to be mapped into the time slots with different sizes. This study develops a Packet Fragmentation with Variable-size and Vigorous-mapping (PFV2) technique based on the following three innovations: (1) we implement variable-size packet fragmentation, which iteratively divides the un-schedulable TT flow into smaller packets and then dynamically reschedules these packets;(2) we implement the vigorous mapping solution from packets to time slots by deeply searching for available time slots within the flow's deadline;and (3) we verify PFV2 based on the LS1028A with Cortex-A72 (i.e., the NXP automotive-grade development board). PFV2 improves the acceptance ratio by up to 20.18% and bandwidth utilization by up to 7.024% compared with the state-of-the-art solution. The theoretical and practical co-verification experiments demonstrate that the PFV2 can effectively improve the flow acceptance ratio and outperf

关键词： Bandwidth

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EAT: Towards Long-Tailed Out-of-Distribution Detection

arXiv

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arXiv 2023年

作者： Wei, Tong Wang, Bo-Lin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://***/Stomachache/Long-Tailed-OOD-Detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Enhanced Encrypted Traffic Classification Through Packet-Flow Dual Views

Enhanced Encrypted Traffic Classification Through Packet-Flo...

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2025 IEEE Wireless Communications and networking Conference, WCNC 2025

作者： Feng, Beibei Wang, Qi Yijing, Wang Li, Feifan Xu, Xiaolin Zang, Tianning Ma, Jingrun Coordination Center of China National Computer Network Emergency Response Technical Team Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China Assets Cybersecurity Technology Co. Ltd. Beijing China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798350368369

In the field of cybersecurity, encrypted traffic classification is of paramount importance, serving as a crucial technology for ensuring data privacy and defending against cyber attacks. Existing ETC (Encrypted Traffic Classification) methods commonly face three challenges in real-world Mobile Internet environments, particularly pertinent to the multimedia context: 1) singular entity traffic information, 2) fixed-size feature filters in learning models, and 3) reliance on traditional encryption protocols. To address these, we introduce the innovative Packet-Flow Dual Views (PFDV) framework. PFDV aims to push the performance boundaries of ETC tasks in multimedia traffic scenarios by examining encrypted payloads separately from the packet and flow views. More specifically, PFDV employs a dual attention framework, comprising both payload attention and gram attention, to accurately identify nuanced differences in network traffic. Such an approach underscores granular packet-level details while offering a comprehensive flow view with packet positional data, striving for an optimal balance in multimedia traffic analysis. PFDV demonstrated superior performance across diverse tasks, achieving accuracy rates and F1 scores significantly higher than competing methods, with results of up to 98.34% accuracy and 95.69% F1 score in multimedia traffic classification. Furthermore, ablation studies demonstrate each component in PFDV contributes significantly to the overall enhancement of ETC performance in the model. © 2025 IEEE.

关键词： Cyber attacks

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An Experimental Study of Unsupervised Rank Aggregation Methods in World University Rankings

An Experimental Study of Unsupervised Rank Aggregation Metho...

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Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Shiwei Feng Qi Deng Siyi Wang Lin Song Chao Liang Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software (NERCMS) School of Computer Science Wuhan University

Recently, more and more college ranking systems are receiving attention due to the demand and necessity of higher education and college ranking system is a key topic in the field of social choice. However, these rankings have different evaluation criteria that lead to confusion for decision-makers. To address this issue, a simple and practical approach is to aggregate these ranking systems from different sources. In this paper, we conduct an experimental study on aggregation of world university ranking. Specifically, we first classify unsupervised RA methods. Then, we compare the aggregation effects of 28 unsupervised RA methods on five public university rankings.

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NNVISR: Bring Neural network Video Interpolation and Super Resolution into Video Processing Framework

arXiv

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arXiv 2023年

作者： Tong, Yuan Hu, Mengshun Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University China

We present NNVISR - an open-source filter plugin for the VapourSynth1 video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://***/tongyuantongyu/vs-NNVISR. © 2023, CC BY-SA.

关键词： Interpolation

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A dual-archive niche with two-stage directed differential evolution for multimodal multi-objective optimization

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The Journal of Supercomputing 2025年第8期81卷 1-34页

作者： Chen, Yilin Gao, Bo Lu, Tao Wu, Yiqi Liao, Xiangyun Wang, Qiong School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science China University of Geosciences Wuhan China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Multimodal Multi-Objective Optimization Problems (MMOPs) refer to situations where there are multiple equivalent Pareto sets corresponding to the same Pareto front. The complexity of solving MMOPs lies in locating multiple equivalent PSs within the decision space while maintaining diversity and convergence in both the decision and objective spaces. To better address this issue, a dual-archive niche approach using two-stage directed differential evolution is proposed, named MMOHCDE_DDM. Firstly, the algorithm leverages the concept of dual archives and introduces affinity propagation clustering for niche differential evolution, automatically generating multiple stable niches. Subsequently, it combines with a competitive particle swarm optimizer to address two solution spaces with different evolutionary requirements. Secondly, by generating multiple subpopulations through affinity propagation clustering, the algorithm employs two distinct mutation strategies to update populations in parallel. This approach enables more effective exploration within niches, effectively enhancing the diversity of both the decision and objective spaces. Furthermore, while the above strategies effectively enhance diversity, they may lead to a loss of convergence. To mitigate this issue and improve convergence, a two-stage directed differential operator is proposed. Introducing a directed differential operator in the later stages of the search provides directed exploration of regions with higher optimality, thereby enhancing convergence. The proposed approach enhances the diversity of both the decision and objective spaces while ensuring convergence in the objective space. The algorithm is compared with various state-of-the-art MMO algorithms on 22 MMO test problems. Experimental results demonstrate that the proposed algorithm outperforms multiple state-of-the-art MMO algorithms in the majority of MMO test problems.

关键词： Multiobjective optimization

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Who, What and Where: Composite-semantic Instance Search for Story Videos

Who, What and Where: Composite-semantic Instance Search for ...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiahao Guo Chao Liang Zhongyuan Wang National Engineering Research Center for Multimedia Software (NERCMS) Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University

This paper studies Who-What-Where (3W) composite-semantic video instance search (INS) problem, which aims to find a specific person doing a queried action in a particular place. Mainstream approaches adopt a complete decomposition strategy, which divides a composite-semantic query into multiple single-semantic queries. However, due to the lack of necessary correlation analysis among constituent semantics, these methods cannot always generate identity-matching and semantics-consistent 3W INS results. To address the above challenges, we propose a partial decomposition scheme with action as the link. Specifically, we selectively split the 3W INS as person-action INS and action-location INS. The former ensures the retrieved person and action share the same identity by modeling their relative spatial positions at the frame level, while the latter improves the semantic consistency between action and location with a cross-semantic attention mechanism at the shot level. Particularly, we build a large-scale 3W INS dataset, containing over 470k video shots, on basis of NIST TRECVID 2016-2021 INS tasks and verify the effectiveness of the proposed method with both quantitative and qualitative experiments.

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Revocable Registered Attribute-Based Encryption With User Deregistration

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IEEE Internet of Things Journal 2025年

作者： Li, Jiguo Chen, Shaobo Lu, Yang Ning, Jianting Shen, Jian Zhang, Yichen Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Nanjing Normal University School of Computer and Electronic Information Nanjing210023 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Many businesses are putting their sensitive data in the cloud with the fast growth of cloud computing and storage. To ensure user privacy, it is necessary to keep encrypted data only in the cloud. Attribute-based encryption (ABE) is a popular mean in cloud storage scenarios. ABE is not only faced with key escrow problem but also suffers from user revocation issue when he or she is no longer authorized to access to encrypted data. In order to address these two issues, we propose a revocable registered attribute-based encryption scheme, which not only avoids key escrow problem but also supports precise revocation of a user’s access to a file as well as permanent deregistration of a user from the system. Furthermore, we prove the semantic security of the scheme and conduct a performance experiment to show the efficiency. © 2014 IEEE.

关键词： Sensitive data

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Adapting Knowledge for Few-Shot Table-to-Text Generation

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 359-371页

作者： Zhixin Guo Mingxuan Yan Jiexing Qi Jianping Zhou Ziwei He Guanjie Zheng Xinbing Wang Chenghu Zhou School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Satellite Network Research Institute Company Ltd. Shanghai Key Laboratory of Satellite Network and State Key Laboratory of Satellite Network Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China Institute of Geographic Sciences and Natural Resources Research Beijing China

Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the topological gap between tabular data and text, especially in real-world applications with limited resources. To mitigate the limitation of insufficient labeled data, we propose a novel framework: Adapt-Knowledge-to-Generate (AKG). The core insight of AKG is to adapt unlabeled domain-specific knowledge into the model, which brings at least three benefits: 1) it injects representation of normal table-related descriptions to bridge the topological gap between tabular data and texts; 2) it enables us to use large amounts of unlabeled domain-specific knowledge fully, which can alleviate the PLMs' inherent shortcomings of lacking domain knowledge; 3) it allows us to design various tasks to employ the domain-specific knowledge. Extensive experiments and analyses are conducted on three open-domain, few-shot natural language generation (NLG) data sets: Humans, Songs, and Books. Compared to previous state-of-the-art approaches, our model achieves superior performance in terms of both fluency and accuracy as judged by human and automatic evaluations.

关键词： Task analysis Video games Prototypes TV Natural language generation Generators Urban areas

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