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ENS-RFMC: An Encrypted network Traffic Sampling Method Based on Rule-Based Feature Extraction and Multi-hierarchical Clustering for Intrusion Detection 27th

ENS-RFMC: An Encrypted Network Traffic Sampling Method Based...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Wei, Zi-Xuan Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031783821

Efficiently and quickly identifying malware in encrypted network traffic is a major challenge. This research presents and evaluates a novel approach for sampling encrypted network traffic, called ENS-RFMC. Initially, the method employs a rule-based strategy for extracting relevant metadata features of encrypted traffic from network connections, SSL and certificates log files. Subsequently, a multi-hierarchical clustering algorithm is utilized to divide the dataset into smaller clusters, with benign clusters subsequently eliminated for streamline data processing. Classifier models such as Random Forests, XGBoost and SVM are then utilized to detect attacks and assess performance. Experimental results indicate that the proposed ENS-RFMC sampling method is effective, with the encrypted traffic intrusion detection model employing ENS-RFMC sampling exhibits enhanced accuracy and efficiency in identifying attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： network intrusion

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th International Conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Federated learning

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Power control and task offloading strategies for high-density wireless body area networks based on deep reinforcement learning

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computer networks 2025年 267卷

作者： Liao, Yang Zhou, Huayang Leng, Chengfeng Su, Zhenlang Qin, Tuanfa School of Computer and Electronic Information Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning530004 China

With the widespread application of Wireless Body Area network (WBAN) in healthcare, issues such as a lack of computational resources and communication interference in high-density WBAN scenarios have become increasingly prominent. To address these issues, this paper introduces Mobile Edge Computing (MEC) to tackle the lack of computational resources and employs power control to mitigate communication interference among WBANs. We describe the joint optimization problem of power control and task offloading in high-density WBAN scenarios as a Markov Decision Process (MDP) and propose a Power Control and Task Offloading (PCTO) algorithm based on the Deep Deterministic Policy Gradient (DDPG) method to achieve coordinated optimization of transmission power and task offloading. Furthermore, we improve the algorithm's learning efficiency and sample utilization by incorporating a prioritized experience replay mechanism. By constructing a multi-objective reward function that comprehensively considers delay, energy consumption, and Signal-to-Interference-plus-Noise Ratio (SINR), and incorporating Quality of Service (QoS) constraints, the algorithm is capable of making intelligent decisions in complex wireless communication environments. The simulation results demonstrate that, compared to existing methods, the proposed approach significantly reduces system delay while improving SINR and service satisfaction in various WBAN density scenarios. © 2025 Elsevier B.V.

关键词： Deep reinforcement learning

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SMDPG: Identifying Metabolite-Disease Associations via Optimized Negative Sampling and Sparse Graph Convolutional network

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 672-683页

作者： Yiran Huang Qiulong Pu Wei Lan Cuiyu Huang Cheng Zhong School of Computer Electronics and Information Guangxi University Nanning China Guangxi Universities Key Laboratory of Parallel and Distributed Computing and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China College of Chemistry Nankai University Tianjin China

Identifying disease-associated metabolites could provide critical clues for the diagnosis and treatment of diseases. Although computational approaches have been proposed to predict disease-associated metabolites by training models using positive and negative samples, few efforts have paid attention to optimize the reliability of negative samples, which could possibly improve the prediction accuracy of model. In this work, we propose a novel method called SMDPG to leverage optimized negative sampling and sparse graph convolutional network to predict metabolite-disease associations. In SMDPG, we first build a metabolite-disease bipartite graph based on less similar metabolites and diseases and propose a negative sampling method to select reliable negative metabolite-disease samples from the bipartite graph to reduce the effect of noisy samples. Then a homogeneous metabolite-disease pair graph is constructed based on the selected negative samples and known metabolite-disease associations. Further, an edge sparseness operation is designed to simplify the connection of the homogeneous graph. Finally, the simplified homogeneous graph is fed into the graph convolutional network to predict metabolite-disease associations. Experimental results show that SMDPG can more accurately predicts metabolite-disease associations as compared to the existing methods. Moreover, the study case demonstrates that SMDPG is an effective framework for identifying potential metabolite-disease associations.

关键词： Diseases Feature extraction Semantics Databases Reliability Graph convolutional networks Training Sparse matrices Predictive models Bipartite graph

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AI-Enabled Multimedia Semantic Communications

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IEEE Communications Magazine 2025年第6期63卷 66-73页

作者： Chenxing Li Yiping Duan Liejun Wang Xiaoming Tao Department of Electronic Engineering Tsinghua University China State Key Laboratory of Space Network China Beijing National Research Center for Information Science and Technology China School of Computer Science and Technology Xinjiang University China

With increasing demand for mobile communication and the development of artificial intelligence, the development of the sixth-generation mobile communication network (6G) will be oriented toward more intelligent and multimodal services. It is necessary to consider the deep integration of information, communication, and data technologies and build a full life cycle resource management framework for computing, data, artificial intelligence models, and communication. To address the requirements of 6G intelligent applications and multimodal services, this article refers to the visual cognitive mechanism of the human brain to improve multimedia representation and communication efficiency and studies the architecture of semantic communication systems, privacy protection mechanisms, and quality evaluation and guarantee mechanisms. The “semantic-network-semantic” architecture was established to integrate semantic computing, intelligent communication, and endogenous security to improve transmission efficiency, service quality, and security credibility. Through prior semantic knowledge, semantic representation, and encoding transmission methods, the quality of service of semantic communication is improved. To build an efficient 6G semantic communication paradigm and system, the communication mode should be changed, and new ideas for future mobile communication should be provided.

关键词： 6G mobile communication Solid modeling Privacy computer architecture Semantic communication Brain modeling Encoding Security Multimedia communication Protection

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Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution

arXiv

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arXiv 2025年

作者： Zhu, Jizhao Shi, Akang Li, Zixuan Bai, Long Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science Shenyang Aerospace University Shenyang China

In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset, a comprehensive evaluation, and a feasible solution. Existing robust benchmark datasets have two key limitations: 1) They generate only a limited range of perturbations for a single Information Extraction (IE) task, which fails to evaluate the robustness of UIE models effectively;2) They rely on small models or handcrafted rules to generate perturbations, often resulting in unnatural adversarial examples. Considering the powerful generation capabilities of Large Language Models (LLMs), we introduce a new benchmark dataset for Robust UIE, called RUIE-Bench, which utilizes LLMs to generate more diverse and realistic perturbations across different IE tasks. Based on this dataset, we comprehensively evaluate existing UIE models and reveal that both LLM-based models and other models suffer from significant performance drops. To improve robustness and reduce training costs, we propose a data-augmentation solution that dynamically selects hard samples for iterative training based on the model’s inference loss. Experimental results show that training with only 15% of the data leads to an average 7.5% relative performance improvement across three IE tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Information retrieval

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UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning 27th

UAD-DPL: An Unknown Encrypted Attack Detection Method Based ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031781681

In the field of cybersecurity, a significant concern is the potential for unknown attacks to occur at any moment. The primary obstacle faced by intrusion detection systems lies in the identification of unknown attacks and the differentiation of such threats from benign network traffic and known attack patterns. Many current deep learning approaches struggle to discern the underlying features of these new attack types, especially in the case of encrypted attacks, resulting in inherent inaccuracies. To tackle these challenges, a novel unknown attack detection method based on deep prototype learning (UAD-DPL) is proposed to improve the accuracy of unknown encrypted attack detection. Firstly, a spatiotemporal fusion feature extraction network for encrypted traffic is employed to enhance the feature representation ability. Subsequently, an innovative prototype-based deep feature space learning model is proposed, which utilizes discriminative loss and open loss training to ensure that the learned encrypted traffic features meet the predetermined prior distribution. Finally, an unknown attack recognition approach is designed to effectively identify both known and unknown attacks, and a three-stage training strategy is proposed to train the model. Experimental results indicate that the UAD-DPL method is highly proficient in detecting both known and unknown attacks in encrypted traffic. Moreover, UAD-DPL exhibits promising application potential in open-set network intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： network intrusion

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Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction

arXiv

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arXiv 2025年

作者： Liu, Wenxuan Li, Zixuan Bai, Long Zuo, Yuxin Xu, Daozhu Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an China

Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4% in event detection and 6.1% in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9% improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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Multi-photon neuron embedded bionic skin for high-precision complex texture and object reconstruction perception research

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Opto-Electronic Advances 2025年第2期8卷 29-44页

作者： Hongyu Zhou Chao Zhang Hengchang Nong Junjie Weng Dongying Wang Yang Yu Jianfa Zhang Chaofan Zhang Jinran Yu Zhaojian Zhang Huan Chen Zhenrong Zhang Junbo Yang College of Science National University of Defense TechnologyChangsha 410073China Key Laboratory of Multimedia Communication and Network Technology in Guangxi School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and DevicesNational University of Defense TechnologyChangsha 410073China College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China

Attributable to the complex distribution of tactile vesicles under the skin and the ability of the brain to process specific tactile parameters(shape,hardness,and surface texture),human skin can have the capacity for tactile spatial reconstruction and visualization of complex object geometry and surface ***,current haptic sensor technologies are predominantly point sensors,which do not have an interlaced distribution structure similar to that of haptic vesicles,limiting their potential in human-computer interaction ***,we report an optical microfiber array skin(OMAS)imitating tactile vesicle interlaced structures for tactile visualization and object reconstruction *** device is characterized by high sensitivity(−0.83 N/V)and fast response time(38 ms).We demonstrate that combining the signals collected by the OMAS with appropriate artificial intelligence algorithms enables the recognition of objects with different hardnesses and shapes with 100%*** also allows for the classification of fabrics with different surface textures with 98.5%accuracy and Braille patterns with 99%*** a proof-of-concept,we integrated OMAS into a robot arm to select mahjong among six common objects and successfully recognize its suits by touch,which provides a new solution for tactile sensory processing for human-computer interaction.

关键词： multiphoton neurons human-computer interaction tactile sensing tactile imaging tactile spatial reconstruction

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FM-FCN:A Neural network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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