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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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Implicit Scene Context-Aware Interactive Trajectory Prediction for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5461-5477页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. © 2016 IEEE.

关键词： Trajectories

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises 42

Design and Application of a One-Stop Mobile Application Shar...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Xinzhe Chen, Luefeng Hu, Jie Yang, Qingfeng Wu, Min Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada

ISBN: (纸本)9789887581543

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Informatization

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Research on the Method of Handling Missing ETC Transaction Data 5th

Research on the Method of Handling Missing ETC Transaction D...

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5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Wu, Songyang Zou, Fumin Guo, Feng Cai, Qiqin Luo, Yongyu Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China School of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China

ISBN: (纸本)9789819908479

A model based on the random forest is constructed to repair the missing trade times in ETC transaction data. The driving speed and traffic volume characteristics of the vehicles in the ETC transaction data are analyzed, while the driving speed of the missed transaction vehicles, and the distance of the road section where they are located, are combined as input features to repair the missing transaction time. A one-day transaction data of a province is used to test. The analysis results show that the random forest model has a better restoration effect and has a smaller mean absolute error and root mean square error;its mean absolute error is 2.71 s, the highest accuracy among the compared methods, and the data are more accurate and complete after interpolation using the random forest model. This paper suggests that the research based on ETC transaction data should first adopt the processing method in this paper to repair the missing trade time in the transaction data to improve the integrity of the data used and ensure the validity and accuracy of the relevant calculation results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Commerce

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning 39

Modeling Inter-Intra Heterogeneity for Graph Federated Learn...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Application of Augmented Reality and 3D Reconstruction Technologies in Power Scenarios 17

Application of Augmented Reality and 3D Reconstruction Techn...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Xu, Mingkai Li, Cong Liu, Chunming Li, Xin Yang, Guodong Yu, Dong Zhang, Chunjie Jinan Power Supply Company State Grid Shandong Electric Power Company Operation and Maintenance Department Jinan China Institute of Automation Chinese Academy of Sciences Cas Engineering Laboratory for Intelligent Industrial Vision Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350387384

As the power system continues to develop, the application of augmented reality (AR) and 3D reconstruction technologies in power scenarios is becoming increasingly widespread. This paper proposes the integration of these two technologies for the detection, maintenance, and fault diagnosis of power equipment. Through real-time video segmentation and detection operations, AR technology can overlay key information onto the field view, helping engineers quickly identify equipment status and potential issues. Simultaneously, 3D reconstruction technology can generate precise models of power equipment and environments, providing comprehensive visual references and analytical tools that enhance maintenance efficiency and safety. The combined application of these technologies can significantly reduce the risks and costs associated with manual operations. © 2024 IEEE.

关键词： 3D Reconstruction Augmented reality Image Segment Objection detection

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CSDNet: Contrastive Similarity Distillation Network for Multi-lingual Image-Text Retrieval 12th

CSDNet: Contrastive Similarity Distillation Network for Mul...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Lu, Shichen Guo, Longteng He, Xingjian Zhu, Xinxin Liu, Jing Liu, Si School of Computer Science and Engineering Beihang University Beijing100191 China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China Institute of Artificial Intelligence Beihang University Beijing100191 China

ISBN: (纸本)9783031463105

Cross-modal image-text retrieval is a crucial task in the field of vision and language, aimed at retrieving the relevant samples from one modality as per the given user expressed in another modality. While most methods developed for this task have focused on English, recent advances expanded the scope of this task to the Multi-lingual domain. However, these methods face challenges due to the limited availability of annotated data in non-English languages. In this work, we propose a novel method that leverages an English pre-training model as a teacher to improve Multi-lingual image-text retrieval performance. Our method trains a student model that produces better Multi-lingual image-text similarity scores by learning from the English image-text similarity scores of the trained teacher. We introduce the contrastive loss to align the two different representations of the image and text, and the Contrastive Similarity Distillation loss to align the Multi-lingual image-text distribution of the student with that of the English teacher. We evaluate our method on two popular datasets, i.e., MS-COCO and Flickr-30K, and achieve state-of-the-art performance. Our approach shows significant improvement over existing methods and has potential for practical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Distillation

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Transmission Line Vibration Measurement by Spacer Matching 17

Transmission Line Vibration Measurement by Spacer Matching

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Pan, Huichao Li, Cong Li, Xin Liu, Chunming Yang, Guodong Zhang, Chunjie Jinan Power Supply Company State Grid Shandong Electric Power Company Operation and Maintenance Department Jinan China Institute of Automation Chinese Academy of Sciences Cas Engineering Laboratory for Intelligent Industrial Vision Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350387384

Power transmission lines are often subjected to wind-induced vibrations (also known as transmission line galloping). To alleviate this problem, quantitative transmission line galloping measurement is necessary. While it is costly and dangerous to install various types of sensors, it is more efficient to use contactless measurement methods (e.g. camera surveillance). In this paper, we propose a novel transmission line galloping measurement method using video analysis technique by spacer matching. First, we determine the spacers by template matching. Then we used matched spacers for the reconstruction of the movement of spacers. We also explore the intra-relationships of the movements of different spacers for accurate measurement. Finally, we synchronize the spacers of the whole line to simulate the galloping of transmission lines. Experiments on several transmission line galloping videos demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Template matching

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GAN-Based Deblurring Algorithm for Drug Package Detection

GAN-Based Deblurring Algorithm for Drug Package Detection

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Chinese Control Conference (CCC)

作者： Hao Yang Zixiang Fei Wenju Zhou Minrui Fei Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

With the development of Industry 4.0, many manufacturers are moving in the direction of automation and intelligence. Drug package inspection quality testing methods have also changed from traditional manual inspection to machine vision inspection. However, due to the long exposure time or vibration of the camera, the images are blurred, which will seriously affect the accuracy of the defect detection. We propose a deblur model with higher level of image restoration and more lightweight. Using reserving and merging blocks to replace the original reserving and merging blocks, which can be used in the inference phase to turn ResBlocks into equivalent straight structures using reserving and merging operations, making the network lighter. The pyramid squeeze attention module is also incorporated to enable the model to efficiently extract finer-grained multi-specimen scale spatial information, thus making more efficient use of feature space information. Finally, a drug package dataset was produced and the model was trained to obtain good deblurring results. The comparative experiments confirmed that the model has better image reduction and excellent reduction speed, and can be further cited in production inspection pipelines to improve detection accuracy.

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