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Early warning model for industrial internet platform based on graph neural network and time convolution network

Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2399-2412页

作者： Guo, Chang Pi, Dechang Cao, Jianjun Wang, Xixuan Liu, Hao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Sixty-Third Research Institute National University of Defense Technology Jiangsu Nanjing21007 China

The control system is an important part of the industrial infrastructure. Once the abnormal is likely to cause equipment failure, production interruption and other serious consequences, a timely and effective anomaly detection method is of great significance to the industrial control system. This paper presents a predictive maintenance strategy–early warning model that includes two modules, prediction and alarm, which can also realize online status monitoring. In the prediction module, a two-channel input model using a graphical neural network and a temporal convolutional network, and the graph structure learning module in the graph neural network is improved to optimize the graph structure. For the alarm module, combining expert experience and mathematical statistics knowledge, a hierarchical alarm mechanism based on abnormal scores and abnormal interval accumulation is proposed. We use the real data to verify the performance of the prediction algorithm and the effectiveness of the early warning index. Experimental results with the real data and the open data show that the prediction performance of the proposed early-warning model is better than the state-of-the-art methods. On the real TRT dataset, the evaluation indicator MAPE is 6.5693%, and on the open dataset, MAPE is 1.3368%. At the same time, its early-warning performance is better than the existing automatic interpretation and manual interpretation methods on the industrial internet. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Graphic methods

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A Meta Distribution-Based Fine-Grained Analysis for Contention-based WiFi Backscatter Networks 21

A Meta Distribution-Based Fine-Grained Analysis for Contenti...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Yulei Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Luo, Yu Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology 999078 China College of Electronics and Information Engineering South-Central Minzu University Wuhan430074 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

WiFi backscatter communication has gained many applications, but its performance characteristics remain to be analyzed. While existing research has investigated the success probability of backscatter tags in contention-based WiFi backscatter networks (CWBNs), it has focused solely on the first-order statistic of the signal-to-interference-plus-noise ratio (SINR). In this paper, we present a meta distribution-based fine-grained analysis that provides high-order statistics of SINR and characterizes the disparity among backscatter transmission links in CWBNs. Leveraging stochastic geometry, we, for the first time, derive mathematical expressions for the b-th moments of conditional success probability and its meta distribution. The extensive Monte-Carlo simulation results validate the accuracy of our proposed theoretical model and demonstrate its outstanding value to help us understand the overall performance of CWBNs. © 2024 IEEE.

关键词： Wi-Fi

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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Treat Randomness with Care: Breaking and Provably Fixing Anonymity of Tor Proposal 295

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IEEE Internet of Things Journal 2025年

作者： Chao, Daichong Zhu, Liehuang Xu, Dawei Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Changchun University College of Computer Science and Technology Changchun130022 China

Tor proposal 295, currently the only open proposal for onion encryption, emerged from a work presented at CRYPTO 2017 and has garnered significant attention from the Tor team for its strong security and efficiency base. However, upon closer examination, it is evident that the design of proposal 295 exhibits a critical flaw—namely, the randomness within the intermediate onion layers is incomplete. This incompleteness renders proposal 295 non-compliant with the Tor-specific anonymity notion known as CircuitHiding, introduced at EUROCRYPT 2018. In this paper, we highlight this anonymity flaw that trivially breaks the CircuitHiding notion. We propose a lightweight fix and rigorously prove the security of the corrected design. To our knowledge, this paper provides the first formal treatment of proposal 295, contributing to Tor’s cryptographic infrastructure. Given that onion encryption has gained prominence as a critical tool for privacy-preserving Internet of Things (IoT) applications, a thorough analysis of its anonymity property is crucial for securing these applications. © 2014 IEEE.

关键词： Anonymity

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Enhancing Multi-label text classification using adaptive promptify concepts 15

Enhancing Multi-label text classification using adaptive pro...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Veerababu Uppukonda, Usha Rani Veeranjaneyulu, N. Vignan's Foundation for Science Technology & Research Dept. of Computer Science & Engineering Guntur India Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India Vignan's Foundation for Science Technology & Research Dept. of Information Technology Guntur India

ISBN: (纸本)9798350370249

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt Engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models. © 2024 IEEE.

关键词： Fine-Tuning GPT Multi-Label Text classification NLP (Natural Language Processing) Pre-trained models Prompt Engineering Transformers

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Cold Start or Hot Start? Robust Slow Start in Congestion Control with A Priori Knowledge for Mobile Web Services 24

Cold Start or Hot Start? Robust Slow Start in Congestion Con...

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33rd ACM Web Conference, WWW 2024

作者： Zhang, Jia Tong, Haixuan Dong, Enhuan Qian, Xin Xu, Mingwei Li, Xiaotian Meng, Zili Zhongguancun Laboratory Beijing China Zhili College Tsinghua University Beijing China Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Meituan Inc. Shanghai China Zhongguancun Laboratory Institute for Network Sciences and Cyberspace Department of Computer Science and Technology Tsinghua University Beijing China Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400701719

Mobile web services value a quick loading of contents in the first page, which is quantified by the above-the-fold time of the first page (first AFT) and is likely to fall into the slow start phase in congestion control. However, the widely deployed slow start mechanism is "cold start", which manually hardcodes the parameters and is not suitable for the first AFT of heterogeneous mobile web services. We revisit the slow start mechanism and find that it could be optimized with a priori knowledge. However, blindly relying on a priori knowledge is not robust enough to handle the fluctuating mobile networks and unpredictable application traffic. In this paper, we propose WiseStart, a "hot-start-based"slow start mechanism. WiseStart utilizes the priori knowledge to set the initial parameters, continuously probes the new connection to handle the fluctuating network conditions, and carefully adapts to the application-limit scenarios. We implement WiseStart in a popular mobile web service online in production. Comprehensive experiments demonstrate that WiseStart reduces the First AFT by 25.43% and the average RCT at connection establishment by 16.15% compared to the default slow start mechanism and other state-of-the-art baselines. © 2024 ACM.

关键词： Web services

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PANSA: A Framework for Threat Knowledge Graph Construction 3

PANSA: A Framework for Threat Knowledge Graph Construction

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Chen, TianXiang Zhuang, Yi Xu, Tao Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9798331506582

Cyber intelligence provides new prevention ideas for cyberattacks, but it is difficult to use effectively due to its multi-source heterogeneity and fragmentation. To address this problem, we propose a threat knowledge graph construction framework based on Pretrained Adversarial NER and relationship Subgraph-based Alignment called PANSA. First, the cyber threat domain ontology model is constructed in combination with the existing cyber threat intelligence standards. Secondly, SecBABC threat entity recognition model is designed, which incorporates pre-training and adversarial training modules to extract high-quality threat intelligence data. Then the Multi-Relation GAT for Entity Alignment framework (MRGEA) is designed. MRGEA considers the impact of different relationships on entities, and uses a multi-relation graph attention network to mine deep information. Finally, the cyber threat knowledge graph is visualized. Experimental results show that PANSA effectively integrates security intelligence and outperforms existing methods in knowledge extraction and entity alignment. © 2024 IEEE.

关键词： Knowledge graph

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A Practical Predefined-Time Stability Criterion and Its Application to Uncertain Nonlinear Systems

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Journal of Systems science & Complexity 2024年第6期37卷 2556-2578页

作者： XU Hao YU Dengxiu SUI Shuai XU Bowen CHEN C.L.Philip Unmanned System Research Institute Northwestern Polytechnical UniversityXi'an 710072China College of Science Liaoning University of TechnologyJinzhou 121001China School of Artificial Intelligence Optics and ElectronicsNorthwestern Polytechnical UniversityXi'an 710072China School of Computer Science and Engineering South China University of Technology and Pazhou LabGuangzhou 510641China

In this paper,the singularity-free predefined-time fuzzy adaptive tracking control problem is studied for non-strict feedback(NSF)nonlinear systems considering mismatched external *** innovative practical predefined-time stability(PPTS)criterion is proposed to provide the theoretical basis for subsequent control *** to the existing predefined-time stability(PTS)criterion,this criterion has a broader application range and can solve the control design issues of nonlinear systems with system *** logic systems(FLSs)are employed to identify unknown nonlinear *** on the backstepping control technology,a singularity-free predefined-time control(PTC)design method is proposed,in which the hyperbolic tangent function is utilized to avoid the singular problem,and the nature of fuzzy basis function is adopted to resolve the algebraic loop *** PPTS of the closed-loop NSF nonlinear system is proven with the Lyapunov theory and the proposed PPTS ***,the efficiency of the presented PTC design method is verified by several sets of simulations on a single link manipulator system.

关键词： Fuzzy adaptive control nonlinear systems practical predefined-time stability predefinedtime control singularity-free control

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Analysis of various Classification Techniques using CNN models for the Detection of Alzheimer's Disease using MRI images 14

Analysis of various Classification Techniques using CNN mode...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hemkiran, S. Sudha Sadasivam, G. Pradeepa, T. Samukthaa, R.S. Ajay Raj, R. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

Alzheimer's disease (AD) is a brain disorder that is associated with memory loss and is typically observed in elderly and aging individuals. This condition is irreversible in nature. Neural networks have shown better performance than traditional machine learning algorithms when it comes to analyzing high-dimensional data from Magnetic Resonance Images (MRI) brain images. This is an approach for processing such data. Detection of Alzheimer's disease is essential because the early treatment can control the progression of the disease in a slower pace. In this work, the proposed model used different models of convolutional neural networks (CNN) techniques to process the MRI images of the brain and classified them into different classes. The proposed model detects AD using neural network feature extraction and classification methods based on the pre-processed datasets. Here, in the proposed model, various algorithms such as Resnet-50, DenseNet-169 and Hyperparameter tuning of Convolutional Neural Network are utilized to find the better performing model in terms of accuracy when compared with other traditional techniques. © 2023 IEEE.

关键词： Image classification

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil Engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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