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An Intrusion Detection Method for Advanced Metering Infrastructure System Based on Federated Learning

Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 927-937页

作者： Haolan Liang Dongqi Liu Xiangjun Zeng Chunxiao Ye School of Electrical and Information Engineering Changsha University of Science and TechnologyChangsha 410114China Hunan Institute of Engineering Xiangtan 411104China

An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to *** detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent *** one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent *** the other hand,large amounts of data are shared over the network and uploaded to a central node for *** processes may compromise data privacy,cause communication delay,and incur high communication *** these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data ***,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative *** optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried *** the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.

关键词： Federated learning(FL) advanced metering infrastructure(AMI)system intrusion detection data concentrator

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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A review of welding simulation methods for large components

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Progress in Natural science:Materials International 2023年第5期33卷 551-568页

作者： Shuai Liu Zhiqiang Wu Wenhao Zhou Honggang Zhou Keke Zhang Danqing Yin Yongping Lei Yongfeng Qiu Engineering Research Center of Mining Development Technology and Equipment for Deep Sea and Earth Ministry of Education Hunan University of Science and Technology Hunan Valin Xiangtan Iron & Steel Group Co. Ltd Research Institute of Technology Jianglu Electromechanical Science & Technology Co. Ltd.Xiangtan School of Materials Science and Engineering Henan University of Science and Technology School of Materials Science and Engineering Beijing University of Technology Guiyang Aluminum Magnesium Design & Research Institute Co. Ltd

The challenges posed by cross-scale effects, large computational requirements, high nonlinearity, and complex interactions of multiple physical phenomena constitute major obstacles to the effectiveness and accuracy of welding simulations for large structural components. Therefore, achieving accurate welding simulation to predict and effectively control welding deformation of substantial engineering structural parts is essential to ensure welding quality and improve production efficiency. This paper reviews the current research status of welding numerical simulation of large engineering components, including the establishment of the finite element model,optimization of the heat source model, selection of appropriate finite element calculation method, and the control of welding deformation through optimization algorithm. In addition, the strategies to improve the precision and efficiency of welding numerical simulation and control welding deformation of engineering components are analyzed and discussed. The future development of welding numerical simulation will focus on balancing simulation accuracy and simulation efficiency and optimizing welding parameters to control welding deformation efficiently.

关键词： Large welded components Algorithm optimization Welding deformation Simulation accuracy and efficiency

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Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

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Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Single-Image SVBRDF Estimation Using Auxiliary Renderings as Intermediate Targets

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 1-15页

作者： Nie, Yongwei Yu, Jiaqi Long, Chengjiang Zhang, Qing Li, Guiqing Cai, Hongmin School of Computer Science and Engineering South China University of Technology Guangzhou Guangdong China Meta Reality Labs USA School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Recently, single-image SVBRDF capture is formulated as a regression problem, which uses a network to infer four SVBRDF maps from a flash-lit image. However, the accuracy is still not satisfactory since previous approaches usually adopt endto-end inference strategies. To mitigate the challenge, we propose “auxiliary renderings” as the intermediate regression targets, through which we divide the original end-to-end regression task into several easier sub-tasks, thus achieving better inference accuracy. Our contributions are threefold. First, we design three (or two pairs of) auxiliary renderings and summarize the motivations behind the designs. By our design, the auxiliary images are bumpiness-flattened or highlight-removed, containing disentangled visual cues about the final SVBRDF maps and can be easily transformed to the final maps. Second, to help estimate the auxiliary targets from the input image, we propose two mask images including a bumpiness mask and a highlight mask. Our method thus first infers mask images, then with the help of the mask images infers auxiliary renderings, and finally transforms the auxiliary images to SVBRDF maps. Third, we propose backbone UNets to infer mask images, and gated deformable UNets for estimating auxiliary targets. Thanks to the well designed networks and intermediate images, our method outputs better SVBRDF maps than previous approaches, validated by the extensive comparisonal and ablation experiments. IEEE

关键词： Regression analysis

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FeBT: A Feature Balancing Transformer for Corporate ESG Forecasting

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4063-4074页

作者： Li, Yawen Zhuang, Mengyu Ye, Guanhua Li, Yan Wang, Junheng Zhou, Jinyi Zhang, Pengfei Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Beijing University of Posts and Telecommunications School of Computer Science National Pilot School of Software Engineering Beijing China University of Electronic Science and Technology of China School of Management and Economics Chengdu China University of Science and Technology Beijing School of Economics and Management Beijing China The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia

Environmental, social, and governance (ESG) serves as a crucial indicator for evaluating firms in terms of sustainable development. However, the existing ESG evaluation systems suffer from limitations, such as narrow coverage, subjective bias, and lack of timeliness. Therefore, there is a pressing need to leverage machine learning methods to predict the ESG performance of firms using their publicly available data. Traditional machine learning models encounter the feature imbalance problem due to the heterogeneity in ESG-related features. Common approaches typically involve unfolding all features, thereby granting highdimensional folding features greater exposure and accessibility to downstream models, which results in the neglect of lowdimensional features. To fill the research gap regarding fully using the heterogeneous features of enterprises to enhance AIbased ESG prediction performance, we propose the Feature Balancing Transformer (FeBT), a model based on autoencoders and Transformer blocks. FeBT incorporates a novel feature balancing technique that compresses and enhances high-dimensional features from imbalanced data into low-dimensional representations, thereby ensuring a more balanced impact of high-dimensional and low-dimensional features on the model's performance in the downstream ESG forecasting module. Extensive experiments verified the superior performance of FeBT compared with stateof-the-art methods in real-world ESG-related datasets and evidenced that our feature balancing module provides significant insights from high-dimensional folding features. © 1989-2012 IEEE.

关键词： Predictive Models Transformers Data Models Forecasting Accuracy Biological System Modeling Training Time Series Analysis Support Vector Machines Vectors ESG Forecasting Data Masking Time Series Forecasting Machine Learning Models Autoencoder High Dimensional Feature Transformer Block Time Series Support Vector Machine Time Series Data Linear Discriminant Analysis Air Quality Long Short Term Memory Recurrent Neural Network Attention Mechanism Final Prediction Transformer Model Word Embedding XG Boost Gated Recurrent Unit Linear Probe Air Quality Index Positional Encoding Multi Head Attention Mechanism Light GBM Traditional Transformation Compressed Representation Time Series Prediction Attention Heads Prediction Model K Nearest Neighbor Imbalance In Levels Latent Representation

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Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks 24

Fake Node-Based Perception Poisoning Attacks against Federat...

引用

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Xiao, Xiong Duan, Mingxing Song, Yingjie Tang, Zhuo Yang, Wenjing College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9798400706011

Federated learning (FL) supports massive edge devices to collaboratively train object detection models in mobile computing scenarios. However, the distributed nature of FL exposes significant security vulnerabilities. Existing attack methods either require considerable costs to compromise the majority of participants, or suffer from poor attack success rates. Inspired by this, we devise an efficient fake node-based perception poisoning attacks strategy (FNPPA) to target such weaknesses. In particular, FNPPA poisons local data and injects multiple fake nodes to participate in aggregation, aiming to make the local poisoning model more likely to overwrite clean updates. Moreover, it can achieve greater malicious influence on target objects at a lower cost without affecting the normal detection of other objects. We demonstrate through exhaustive experiments that FNPPA exhibits superior attack impact than the state-of-the-art in terms of average precision and aggregation effect. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Heat-resistant Al_(2)O_(3) nanowire-polyetherimide separator for safer and faster lithium-ion batteries

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Journal of Materials science & technology 2023年第11期142卷 112-120页

作者： Wenqiang Hu Wenbin Fu Samik Jhulki Liang Chen Aashray Narla Zhijian Sun Fujia Wang Alexandre Magasinski Gleb Yushin School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Institute of Materials Science and Engineering School of Mechanical and Electronic Control EngineeringBeijing Jiaotong UniversityBeijing 100044China Sila Nanotechnologies Inc AlamedaCA 94501USA School of Chemistry and Chemical Engineering Hunan Institute of Science and TechnologyYueyangHunan 414006China

Poor heat/flame-resistance of polyolefin(e.g.,polyethylene and polypropylene)separators and high flammability of organic electrolytes used in today’s lithium-ion batteries(LIBs)may trigger rare yet potentially catastrophic safety ***,we mitigate this challenge by developing a heat-resistant and flame-retardant porous composite membrane composed of polyetherimide(PEI)and Al_(2)O_(3) nanowires(NWs).The membranes are fabricated based on an industrially scalable non-solvent-induced phase separation process,which results in an intimately interconnected porous network of Al_(2)O_(3) NWs and *** produced composite membranes exhibit excellent flexibility,thermal stability,and ***,the composite membranes exhibit minimal thermal shrinkage and superior tensile strength(16 MPa)at temperatures as high as 200℃,significantly exceeding the performance of conventional polyolefin *** with commercial separators,their superior wettability and higher ionic conductivity(by up to 2.4 times)when filled with the same electrolyte,larger electrolyte uptake(-190 wt.%),as well as improved cycle and rate performance demonstrated in LiNiMnCoO_(2)(NCM)-based LIBs make them attractive choices for a variety of electrochemical energy storage devices.

关键词： Lithium-ion batteries Separators Al_(2)O_(3)NWs Polyetherimide Thermal resistance

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A Locality-Sensitive-Hashing-Based Collaborative Recommendation Method for Responsible AI-Driven Recommender Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 393-404页

作者： Lin, Wenmin Zhou, Xinyi Sun, Lu Qi, Lianyong Tsai, Sang-Bing Yang, Yihong Liu, Hanwen Kou, Huaizhen Kong, Lingzhen Hangzhou Normal University Alibaba Business School Hangzhou311121 China College of Computer Science and Technology Qingdao266580 China International Engineering and Technology Institute Hong Kong999077 Hong Kong China University of Geosciences School of Information Engineering Beijing100083 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

As one of the most representative recommendation solutions, traditional collaborative filtering (CF) models typically have limitations in dealing with large-scale, sparse data to capture complex relationships between users and items. The rise of artificial intelligence (AI) provides powerful tools such as deep neural networks to overcome the data sparsity issue with typical CF models. Existing works on AI-driven recommender systems are focusing on improving recommendation accuracy by extracting rich user/item features from multisource data with deep learning tools to build more accurate user preference model. How to ensure the responsibility of AI-driven recommender systems is still a big challenge. On one hand, AI-driven recommender systems need to access raw data such as user profiles to train recommendation model, which face the risk of leaking user privacy information. On the other hand, the ever-increasing volume of user/item interaction records raises the efficiency challenge to provide recommendation results in a real-time manner. In view of those observations, we propose a collaborative recommendation method by adopting the locality-sensitive-hashing (LSH) technique (i.e., DisRecLSH), which aims to achieve the goal of privacy protection and calculation efficiency for AI-driven recommender systems. More specifically, we split DisRecLSH into three phases: 1) offline feature extraction: deep neural networks are applied to extract user/item features from multisource user/item data on each local dataset;2) offline user index building: LSH technique is adopted to map user features to hash codes to quickly recall correlated similar users for a given target user;and 3) online top-N items calculation: with similar users selected by phase 2, top-N items are calculated based on the ranking of predicted user-item rating score. Finally, extensive experiments are conducted on three public datasets to evaluate the efficiency of our proposal. © 2020 IEEE.

关键词： Recommender systems

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Federated Learning Privacy-preserving Method Based on Bregman Optimization 10

Federated Learning Privacy-preserving Method Based on Bregma...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Gengming Zhang, Jiyong Zhang, Shaobo Yin, Yijie Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

Federated learning has received a lot of attention for its ability to solve the data silo problem, but it is also limited by the problem of data heterogeneity and privacy. Non-Independent Identical Distribution (Non-I.I.D) data leads to performance degradation of federation models, and privacy problem have been studied as a hot topic in the field of federated learning. However, current research rarely considers non-I.I.D data and privacy simultaneously. In this paper, we propose a federated learning scheme based on Bregman and differential privacy (FLBDP). Our approach adopts Bregman distance for personalized model training, which aims to control the difference between local model and global model in a limited range, the FLBDP can reduce the model difference to improve the model performance by Bregman optimization. In addition, we use a Gaussian mechanism to perturb the personalized model and update the local model by the perturbed personalized model, which enables the model parameters to satisfy differential privacy in the uplink channel to enhance user privacy protection. © 2023 IEEE.

关键词： Learning systems

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