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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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APT Attack Detection Method Based on Long-Term Feature Extraction from Provenance Graphs 16

APT Attack Detection Method Based on Long-Term Feature Extra...

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16th ieee International conference on Computational Intelligence and Communication networks, CICN 2024

作者： Jiacheng, Li Northeast Electric Power University School of Computer Science Jilin China

ISBN: (纸本)9798331505264

As the frequency of Advanced Persistent Threat attacks rises, cybersecurity threats are becoming increasingly severe. Detecting APTs, a critical method for addressing complex attack behaviors, requires more resilient and efficient approaches. This paper presents a novel detection technique that leverages long-term feature extraction from provenance graphs, utilizing multi-head attention mechanisms and embedding strategies. Our approach first captures long-term feature sequences by exploiting the topological structure of provenance graph networks. A multi-head attention mechanism is then applied to integrate historical data with current local context, enhancing the distinction between normal and malicious behaviors in the feature space. Finally, the K-means clustering algorithm is employed for unsupervised detection of APTs. Experimental results across five popular datasets demonstrate that, compared to three leading APT detection methods, our approach significantly improves detection accuracy and efficiency, offering a more robust solution for addressing complex APT attack scenarios. © 2024 ieee.

关键词： Graph embeddings

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Bearing Fault Diagnosis Based on the Combination of Attention Entropy and Bayesian Network 5

Bearing Fault Diagnosis Based on the Combination of Attentio...

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5th ieee International conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Tong, Zhaojing Fan, Yongkui Tang, Lina School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China School of Training Office Zhengzhou Industry Technicians College Zhengzhou China

ISBN: (纸本)9798331533694

In order to improve the accuracy of bearing fault identification, this paper proposes a bearing fault diagnosis method that combines the Improved Refined Composite Multiscale Attention Entropy (IRCMATE) with Bayesian networks. The method uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the original signal, obtain multiple intrinsic modal functions (IMFs), calculate the Pearson correlation coefficient (PCC) between each IMF and the original signal, and select the IMFs with PCC greater than 0.3 to reconstruct the signal. Aiming at the problem that the multiscale attention entropy depends on the data length and is easily affected by the outliers in the sample, the improved refined composite multiscale attention entropy is proposed. The paper calculates the IRCMATE of the reconstructed signals, and select the IRCMATE under the appropriate scale as the new feature vector, so as to realize the fault feature extraction of the bearings. Improved Crown Porcupine Optimizer (ICPO) is proposed. ICPO solves the variable conflict problem of Crown Porcupine Optimizer (CPO) by realizing binary conversion through Sigmoid function. The dynamic strategy selection factor is introduced into the CPO, which improves the global exploration ability of the algorithm early in the iteration. After each iteration, the local optimization operations of increasing edge, decreasing edge and reversing edge are implemented on the current global optimal solution, so as to avoid falling into the local optimal solution. Using ICPO to get Bayesian network structure, and the bearing fault diagnosis model is obtained after training using the training set. It is shown through experiments that the fault features extracted using IRCMATE are more obvious. The comparison with other methods shows that IRCMATE-ICPO-BN has a higher fault diagnosis recognition rate. © 2025 ieee.

关键词： Bayesian networks

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Secure Remote Access to local Network with Distributed Tunnel Management

Secure Remote Access to Local Network with Distributed Tunne...

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2024 International conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Priyan Kishore, M.S. Chandana Sathwika, U. Malarvel, Muthukumaran Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The proliferation of distributed networks and the increasing demand for remote collaboration and maintenance have highlighted the need for secure and controlled remote access solutions. This paper presents a novel approach to establishing remote network access through a distributed tunneling management system. The proposed methodology addresses the challenges of providing authenticated and controlled access to private networks while facilitating seamless remote collaboration and maintenance tasks. The system architecture comprises a public tunneling server responsible for hosting the tunneling service and a separate management server dedicated to handling client connections, tunnel creation, and termination. Client networks run background processes that communicate with the communication server through websocket connections, enabling tunnel establishment and termination based on authorized requests. The methodology incorporates authentication mechanisms and secure protocols to ensure the confidentiality and integrity of communication channels. The distributed nature of the architecture enhances scalability, allowing multiple client networks to leverage the tunneling service concurrently. Experimental results demonstrate the viability of the implemented protocols, showcasing efficient computation, storage, communication and practical solutions for remote access to local networks, with potential applications across various industries, including IT support, remote collaboration, and maintenance operations while ensuring the confidentiality and integrity of sensitive data. © 2024 ieee.

关键词： Network security

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Chinese Named Entity Recognition with Integrated Channel Attention 5

Chinese Named Entity Recognition with Integrated Channel Att...

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5th International conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Song, Wei Zheng, He Guo, Wei Ning, Keqing School of Information Science and Technology North China University of Technology Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (纸本)9798331528911

Using an Iterated Dilated Convolutional Neural Network (IDCNN) in Chinese Named Entity Recognition (NER) helps capture local information. However, the contribution of information from different positions in a sentence to the judgment of the current position is not uniform, which needs to be considered when expanding a Dilated Convolutional Neural Network (DCNN). To address this, this paper incorporates Efficient Channel Attention (ECA) into DCNN and constructs the It-erated Dilated Convolutional Attention Network (IDCAN). The model focuses on the importance of different channel information and integrates the Linguistically-motivated Bidirectional Encoder Representation from Transformers (LERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF) to form a Chinese NER model. The model uses LERT for word vector construction, BiGRU for capturing temporal features, IDCAN for extracting key local features, and CRF for feature decoding. On the Daily, MSRA and E-commerce Chinese datasets, the model achieved F1 scores of 96.88%, 95.15% and 90.52%, respectively, outperforming other models. © 2024 ieee.

关键词： Convolutional neural networks

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Small Object Detector Using Contextual local Features and Global Representations for Autonomous Driving 26

Small Object Detector Using Contextual Local Features and Gl...

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ieee 26th International conference on Intelligent Transportation Systems (ITSC)

作者： Wu, Xuke Bin Tian Xiong, Gang Song, Bing Ye, Peijun Zhu, Fenghua Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing Peoples R China

ISBN: (纸本)9798350399462

We present a hybrid architecture called Contextual-Enhanced Transformer Network (CEFormer) that leverages both Convolutional Neural networks (CNN) and transformer-style networks for computer vision tasks. CNNs are good at modeling local features due to their local nature and weight sharing, while transformers are good at capturing global contextual features due to their self-attention mechanism. Our CEFormer uses a parallel network structure that combines the strengths of both CNNs and transformers for image feature representation. Specifically, we design an enhanced multi-headed attention module and contextual attention module that extracts and enhances globle features and contextual features on two branches for the task of small target detection in an autonomous driving environment. Moreover, we propose a lightweight cross-branch fusion module that reduces the parameters and computational complexity of the feature interaction. Our CEFormer achieves competitive results in target detection with Mask R-CNN and outperforms ResNet and transformer-based models. It also shows significant improvement over other methods on MS COCO, TT100K, and ImageNet datasets.

关键词： Convolutional neural networks

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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17th International conference on COMmunication Systems and networks, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 ieee.

关键词： Prediction models

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Indoor localization with GravNetConv and Dynamic Graphs 9

Indoor Localization with GravNetConv and Dynamic Graphs

引用

9th International conference on computer Science and Engineering, UBMK 2024

作者： Bayraktar, Mert Özcan, Alper Ulusar, Umit Deniz Akdeniz University Computer Engineering Department Antalya Turkey

ISBN: (纸本)9798350365887

Traditional indoor localization methods often rely on Received Signal Strength Indicator (RSSI) measurements from Wi-Fi access points (APs) and utilize techniques like fingerprinting. However, these methods face challenges due to signal variability and environmental factors, leading to limited accuracy. To address these issues, we propose a novel approach leveraging Graph Neural networks (GNNs) with a dynamic graph generation process using GravNetConv. This method processes input data in a learned lower-dimensional space, using a Gaussian function to emphasize proximate points, thereby capturing complex spatial relationships and improving localization accuracy through more effective local neighbor capture and fine-grained detail recognition. Our experiments show that the GravNetConv-based method outperforms conventional indoor localization approaches, particularly on larger, more diverse datasets, leading to more reliable and precise positioning in dynamic environments. © 2024 ieee.

关键词： Graph neural networks

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Dynamic Controller Assignment in LEO Satellite based SDN using Multi-Agent Deep Reinforcement Learning

Dynamic Controller Assignment in LEO Satellite based SDN usi...

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2024 ieee conference on Standards for Communications and Networking, CSCN 2024

作者： Mokhtar, Hamza Di, Xiaoqiang Jiang, Zhengang Hamdan, Mosab Changchun University of Science and Technology School of Computer Science and Technology Changchun China Zhongshan Institute Changchun University of Science and Technology Zhongshan China Walton Institute for Information and Communication Systems Science South East Technological University Waterford Ireland

ISBN: (纸本)9798331507428

With the rapid growth of Low Earth Orbit (LEO) satellite network applications and the accelerated expansion of the network size, software-defined networking (SDN)-based LEO satellite networks are introduced to manage resources efficiently. These networks face significant challenges due to the dynamic and time-varying nature of LEO systems, such as traffic fluctuations and imbalanced loads. Existing approaches struggle to handle efficient controller assignment in these rapidly changing topologies. To address this, we propose a novel Multi-Agent Deep Deterministic Policy Gradient (MADDPG) scheme that extends the actor-critic algorithm for distributed decision-making. The critic utilizes global information, while each actor manages local decisions, optimizing both load balancing and latency. Simulation results demonstrate that our approach significantly reduces latency and achieves better load balancing compared to state-of-the-art techniques. © 2024 ieee.

关键词： Deep reinforcement learning

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Phase-field Models for Lightweight Graph Convolutional networks

Phase-field Models for Lightweight Graph Convolutional Netwo...

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2023 ieee/CVF conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Sahbi, Hichem Sorbonne University CNRS LIP6 ParisF-75005 France

ISBN: (纸本)9798350302493

In this paper, we design lightweight graph convolutional networks (GCNs) using a particular class of regularizers, dubbed as phase-field models (PFMs). PFMs exhibit a bi-phase behavior using a particular ultra-local term that allows training both the topology and the weight parameters of GCNs as a part of a single "end-to-end"optimization problem. Our proposed solution also relies on a reparametrization that pushes the mask of the topology towards binary values leading to effective topology selection and high generalization while implementing any targeted pruning rate. Both masks and weights share the same set of latent variables and this further enhances the generalization power of the resulting lightweight GCNs. Extensive experiments conducted on the challenging task of skeleton-based recognition show the outperformance of PFMs against other staple regularizers as well as related lightweight design methods. © 2023 ieee.

关键词： Topology

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A Propagation Model Based on Benign Worm Confrontation and Monitoring in Peer-to-Peer networks 6

A Propagation Model Based on Benign Worm Confrontation and M...

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6th International conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Xue, Jiali Ren, Jianguo Xu, Yonghong Wang, Lei Jiangsu Normal University School of Computer Science and Technology Xuzhou China Jiangsu Normal University School of Life Sciences Xuzhou China The Yi-Shu-SI River Basin Administration Information Center Inner Mongolia China

ISBN: (纸本)9798350353174

In peer-to-peer (P2P) resource-sharing networks, worms spread rapidly. Currently, the technique of using benign worms against malicious worms is prone to problems such as resource burden and network congestion. Therefore, this paper proposes a new model, namely M-SIBR. First, based on the existing model (SIBR) of benign worms against malicious worms, a monitoring compartment is introduced, i.e. M. The benign infected node feeds back the information of the neighbor list according to the characteristics of the network, and adds the node with the largest infection probability in the current susceptible state to the monitoring state, to better control the spreading of the worm. Secondly, we calculate the equilibrium point and propagation threshold of the new model, and prove the local and global stability of the equilibrium point by using the Routh Hurwitz criterion and the Lyapunov function. Finally, the results of numerical simulation experiments show that the introduction of the monitoring mechanism can effectively contain the spread of the worm. © 2024 ieee.

关键词： Peer to peer networks

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