检索结果-内蒙古大学图书馆

Real-Time Identification Technology for Encrypted DNS Traffic with Privacy Protection

computers, Materials & Continua 2025年第6期83卷 5811-5829页

作者： Zhipeng Qin Hanbing Yan Biyang Zhang Peng Wang Yitao Li School of Computer Science and Engineering Beihang UniversityBeijing100191China National ComputerNetwork Emergency Response Technical Team/Coordination Center of China Shanxi Branch Taiyuan030001China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China

With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become *** encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning ***,these methods still face challenges in real-time performance and privacy *** paper proposes a real-time identification technology for encrypted DNS traffic with privacy ***,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification ***,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification ***,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce *** results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.

关键词： Encrypted DNS edge computing federated learning real-time detection privacy protection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients 10

Enhancing Federated Learning by One-Shot Transferring of Int...

引用

10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Deng, Youxingzhu Zhou, Yipeng Liu, Gang Wang, Jessie Hui Shui, Yu Shenzhen University College of Computer Science and Software Engineering Shenzhen China Macquarie University School of Computing Faculty of Science and Engineering Sydney Australia Institute for Network Sciences and Cyberspace Tsinghua University Peking China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350345032

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement. © 2023 IEEE.

关键词： Privacy-preserving techniques

来源：评论

学校读者我要写书评

暂无评论

Multi-dimensional Classification via Selective Feature Augmentation

引用

Machine Intelligence Research 2022年第1期19卷 38-51页

作者： Bin-Bin Jia Min-Ling Zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina

In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented *** this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented ***, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

关键词： Machine learning multi-dimensional classification feature augmentation feature selection class dependencies

来源：评论

学校读者我要写书评

暂无评论

Malicious Traffic Detection with Noise Labels Based on Cross-Modal Consistency

IEEE Networking Letters

引用

IEEE networking Letters 2024年第2期6卷 148-151页

作者： Yuan, Qingjun Niu, Weina Wang, Yongjuan Gou, Gaopeng Lu, Bin Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives. © 2019 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Perception-Enhanced Generative Transformer for Key Information Extraction from Documents 27th

Perception-Enhanced Generative Transformer for Key Informat...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Runbo Ou Yang, Jun Jie Gao, Chen Qin, Xugong Zeng, Gangyan Hu, Xiaoxu Zhang, Peng School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Viterbi School of Engineering University of Southern California Los Angeles United States National Computer Network Response Technical Team/Coordination Center Technology Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (纸本)9783031781186

Key information extraction (KIE) from scanned documents has attracted significant attention due to practical real-world applications. Despite impressive results achieved by incorporating multimodal information within the generative framework, existing methods fail to understand complex layouts and fuzzy semantics in document images. To settle these issues, we propose a perception-enhanced generative transformer (PEGT), which improves the model through fine-grained multimodal modeling and pre-training tasks tailored for the generative framework. Firstly, we introduce a pre-trained vision-language model to provide transferable knowledge for visual text perceptron. Then two auxiliary pre-training tasks including absolute position prediction (APP) and semantic relationship reasoning (SRR) are designed for the generative framework. APP learns to predict which grids the texts fall into, improving the model on utilization of the position information. SRR exploits prior information of semantic relationships, injecting the ability for better semantic discrimination into PEGT. Finally, well-designed prompts are leveraged to unleash the potential of PEGT for extracting key information from documents. Extensive experiments on several public datasets show that PEGT effectively generalizes over different types of documents. Especially, PEGT achieves state-of-the-art results in terms of F-measure, i.e., 97.47%, 98.04%, and 84.32% on the SROIE, CORD, and FUNSD datasets, demonstrating the superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Joint Subcarrier and Power Allocation in Mobile Scenario of the OFDM Systems Based on Deep Reinforcement Learning 8

Joint Subcarrier and Power Allocation in Mobile Scenario of ...

引用

8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Li, Xiaodong Zhou, Weixi Zhang, Hongjie Zhao, Jing Zhao, Dongcai Dong, Zhicheng School of Information Science and Technology Tibet University Lhasa China School of Computer Science Sichuan Normal University Chengdu China School of Network & Communication Engineering Chengdu Technological University Chengdu China

ISBN: (纸本)9781665456128

The increasing number of base station (BS) service users has made spectrum resources more valuable, leading to the need for efficient resource allocation. To address this issue, this paper proposes a novel deep reinforcement learning (DRL) architecture that solves the subcarrier-user matching and subcarrier power allocation problem in orthogonal frequency division multiple (OFDM) systems. This approach improves the system spectral efficiency (SE) by reducing inter-subcarrier interference (ICI) through subcarrier matching and power allocation, while ensuring that the minimum rate requirements for users (mobile or stationary) are met. We approach this problem by dividing it into two interconnected components: the subcarrier matching part (SMP) and the subcarrier power allocation part (SPAP). To address these two parts, the paper proposes a DRL method based on joint allocation of resources by three agents (JAoR-TA). In SMP, the REINFORCE algorithm is used to match subcarriers, and the results are then given to SPAP. In SPAP, two Actor-Critic network frameworks are proposed to address the subcarrier power allocation problem. Based on the simulation results, it has been observed that the JAoR-TA algorithm outperforms the REINFORCE algorithm in mobile scenarios, as it achieves a higher system SE. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Tools and environment for maximum efficiency in complex telecommunication system 32

Tools and environment for maximum efficiency in complex tele...

引用

32nd Telecommunications Forum, TELFOR 2024

作者： Zlatanovic, Jovan Trivic, Zoran Milosevic, Ivana Milosevic, Mirko Pe Etv Network Operation Department Belgrade Serbia Pe Etv Maintaining the Service Department Belgrade Serbia School of Technical and Art Applied Studies Academy of Electrical and Computer Engineering Department of Electrical Engineering and Computer Science Belgrade Serbia

ISBN: (纸本)9798350391053

The contribution of energy efficiency covers a wide range of interests. Energy efficiency affects climate change mitigation goals, energy saving, health benefits, and productivity. The quick expansion of data traffic and connection has caused the telecommunications industry's energy usage to increase significantly. It operated with equipment that is already energy efficient and it will be try to explain the additional energy saving. It will be shows a methodology that uses several parameters that can be varied in order to achieve the desired high-power transmitter efficiency. The result of the work is the saving of energy and resources through the use of tools that keep components of telecommunication systems in the optimal mode of operation using intelligent application. This application uses an intelligent algorithm that allows us to save approximately 20MW per year. © 2024 IEEE.

关键词： Efficiency high-power transmitter intelligent application optimal mode telecommunication system

来源：评论

学校读者我要写书评

暂无评论

Automatic Pricing and Replenishment Decision of Vegetable Commodities: A Multi-model Perspective 5

Automatic Pricing and Replenishment Decision of Vegetable Co...

引用

5th International Seminar on Artificial Intelligence, networking and Information Technology, AINIT 2024

作者： Ma, Yueyue Li, Boyu Wu, Qing School of Computer Science and Engineering Central South University Changsha China School of Electronic Information Central South University Changsha China Central South University Information and Network Center Changsha China

ISBN: (纸本)9798350385557

In fresh food supermarkets, the shelf life of vegetable products is relatively short. In order to make reliable and reasonable replenishment and pricing decisions, and achieve maximum sales revenue and profit, this article first studies the Pearson correlation coefficient model to study the relationship between various categories of vegetables and the sales volume of individual products;Then, a time series model is established from a vertical perspective combined with ARIMA, and a random forest model is established from a horizontal perspective. Then, the final strategy for predicting the daily replenishment volume and pricing strategy for the next week is obtained by integrating the two models;Finally, considering the limited sales space of vegetable supermarkets, we optimized the machine learning model using gradient descent and proposed a daily replenishment and pricing strategy to maximize profits. © 2024 IEEE.

关键词： Vegetables

来源：评论

学校读者我要写书评

暂无评论

LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System 21

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

引用

21st Annual IEEE International Conference on Sensing, Communication, and networking, SECON 2024

作者： Zhou, Yinnan Zhou, Hao Li, Luowei Chen, Siming Liu, Zhi Li, Xiang-Yang LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn’t be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions. © 2024 IEEE.

关键词： Wi-Fi

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：