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Analysis and Research of Generative Adversarial Network in Anomaly Detection 7

Analysis and Research of Generative Adversarial Network in A...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Zhang, Lin Fan, Fuyou Dai, Yang He, Chunlin China West Normal University School of Electronic Information Engineering Nanchong China Yibin University Faculty of Artificial Intelligence and Big Data Intelligent Terminal Key Laboratory of Sichuan Province Yibin China China West Normal University School of Computer Science Nanchong China

ISBN: (纸本)9781665478571

In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected. © 2022 IEEE.

关键词： Generative adversarial networks

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The Intelligent Smart City Deployment Via Artificial intelligence Software Networking 2

The Intelligent Smart City Deployment Via Artificial Intelli...

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Gupta, Ayan Das Kishore, K. Prasad, Arun B Kannadasan, B. Narayana, M Srinivasa Singh, Prabhdeep Chandernagore Government College Postgraduate Department of Geography West Bengal Hooghly India Chandernagore Government College Affiliated to the University of Burdwan India Voorhees College Department of Commerce Vellore India Institute of Law Nirma University Gujarat Ahmedabad India B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Civil Engineering Vandalur Chennai India Kl Business School K L University Koneru Lakshmaiah Education Foundation Vijayawada India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The Software Defined Network (SDN) structure is a novel approach to network management. Switches in SDN do not process incoming packets in the same way that they do in a traditional network computing environment. They look in the linking tables for incoming packets, and if none are found, they are sent to the control system for processing, which is the SDN's operating system. The most serious threat to cyber security in an SDN network is a Spread Denial of Service (Dodos) attack. The attack will take place at the network or application layers of the infected devices that are linked to the network. In this paper, an intelligent machine learning-based method for detecting whether incoming packets are infected or not is proposed Smart city activities seek to go beyond the limitations of traditional urban planning, which regulates critical infrastructures in silos, and to profit on the pervasiveness of data and services given by electronic technologies such as machine learning, the internet of things, affordable and big data. © 2022 IEEE.

关键词： Machine learning

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DDoS Attack Detection Based on CNN and Federated Learning 9

DDoS Attack Detection Based on CNN and Federated Learning

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9th international conference on Advanced Cloud and big data, CBD 2021

作者： Lv, Dingyang Cheng, Xinyu Zhang, Jinghui Zhang, Wei Zhao, Wei Xu, He School of Computer Science and Engineering Southeast University Nanjing China Digital Department China United Network Communications Group Co. Ltd. China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Computer Science and Technology Anhui University of Technology Hefei China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665407458

Distributed Denial of Service (DDoS) attack, which seriously affects the availability of the Internet, is one of the most dangerous network attacks. Machine learning is widely used in DDoS attack detection. However, due to data privacy and transmission overhead problems, the traffic data distributed on different network devices will not be easily concentrated together, which leads to difficulties in model training. In this paper, we combine federated learning (FL) and neural network and propose a FLDDoS system to counter DDoS attacks. We designed a CNN model and a data preprocessing algorithm to fully mine the features of DDoS traffic data. The federated learning framework is used for the model training, which can make full use of the data and computing performance of the terminal devices, and protect the data privacy. In addition, we also consider non-IID cases of the data and use a model mixing approach to provide a more personalized model for each client. The experimental results show that the proposed FLDDoS has a good performance. The accuracy of DDoS attack detection and multiclass classification is 99% and 90%, respectively. And the personalized method can overcome the non-IID problem of data in FL effectively. The accuracy of the CNN model proposed in this paper is 20% higher than that of the traditional model. Under the condition of using the same model, the model accuracy obtained by FL is basically the same as that obtained by centralized training. © 2022 IEEE.

关键词： Denial-of-service attack

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Research on the construction and application of knowledge graph for process quality optimization 5

Research on the construction and application of knowledge gr...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Qiu, Quan Li, Ran Chen, Bingquan Hu, Ning Key Laboratory of Industrial Equipment Quality Big Data Reliability Data Center MIIT the 5th Electronics Research Institute MIIT Guangzhou China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Process quality optimization is a necessary means for enterprises to improve product quality. However, the current process quality optimization of enterprises is highly dependent on experienced senior technicians. If the experience of these people cannot be effectively converted into knowledge, the value of massive industrial data cannot be effectively obtained. It has become the main bottleneck faced by the intelligent production of enterprises. To this end, this paper proposes a process quality optimization method based on knowledge graph, which uses process quality optimization rules and artificial experience knowledge to construct a process quality optimization knowledge graph including process knowledge, product defect knowledge, and process optimization knowledge. Associate process defects and defect handling cases in the form of event clusters, combine artificial intelligence and graph algorithms, and assist process quality optimization decision-making in various ways such as knowledge search, knowledge question and answer, and knowledge recommendation. © 2022 IEEE.

关键词： Knowledge graph

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A Multi-Head Attention Mechanism Base Multi-Dimensional data Quality Evaluation Model 22

A Multi-Head Attention Mechanism Base Multi-Dimensional Data...

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7th international conference on big data and computing, ICBDC 2022

作者： Liu, Xiaobao Li, Qi Zhu, Shaosong Wang, Cong Meng, Lingzhen State Grid Shandong Electric Power Company Information and Communication Company Jinan China

ISBN: (纸本)9781450396097

High-quality power data is the basis for reliable operation of power systems, efficient data processing, and effective mining of the potential value of power data. How to use big data, artificial intelligence and other technologies to evaluate the quality of power data is a hot research topic in the field of electric power. At present, most of the power data quality evaluation methods are simple and lack the research of general data quality evaluation model. Therefore, this paper proposes a multi-dimensional data quality evaluation model based on a multi-head attention mechanism. The model measures multiple indicators such as completeness, accuracy, smoothness, and correlation. The corresponding methods are used to quantify these indicators to form a data quality evaluation index system oriented to multi-dimensional indicators;then, an application feedback mechanism based on a multi-head attention network is used to correct the calculation weights and score outputs, so as to achieve the evaluation of power data quality. Finally, the validation analysis is carried out based on the electricity data of a region in China. The experimental results show that the proposed method can effectively evaluate the quality of electric power data. © 2022 ACM.

关键词： Quality control

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Evaluation of data poisoning attacks on federated learning-based network intrusion detection system 24

Evaluation of data poisoning attacks on federated learning-b...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Yuemeng Zhang, Yong Zhang, Zhao Bai, Haonan Zhong, Tianyi Song, Mei School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computer and Information Technology Beijing Jiaotong University Beijing100876 China

ISBN: (纸本)9798350319934

Recently, federated learning-based network intrusion detection system (FL-based NIDS) has been considered as an essential tool to protect network security. It enables learning an effective intrusion detection model collaboratively without data privacy leakage. However, FL- based NIDS has exhibited inherent vulnerabilities on the data poisoning attacks launched by malicious clients. In this paper, we conduct the first systematic robustness evaluations of FL-based NIDS under data poisoning attack. Firstly, in consideration of the traffic domain constraints, we design the clean-label data poisoning attack against FL-based NIDS. Specifically, we propose an improved poisoned sample generation model based on Generative Adversarial Network, called PT-GAN, which optimizes with a new loss function that incorporates the feedback of the target intrusion detection model. The minimally-perturbed and correctly-labeled traffic samples generated by PT-GAN are then injected in the local training dataset to corrupt the intrusion detection model. Then, we explore the potential defense mechanism for these data poisoning attacks against FL- based NIDS and propose a novel defense method based on poisoned sample detection. Concretely, we propose the important neuron activations extraction method based on the layerwise relevance propagation method and then apply Oneclass-SVM to detect the poisoned sample. Experiments show that the proposed PT-GAN can degrade the performance of FL-based NIDS up to 28% on UNSW-NB15 dataset. We also demonstrate the robustness of our proposed defense methods against data poisoning attacks. © 2022 IEEE.

关键词： Network security

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FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning 40

FREDIS: A Fusion Framework of Refinement and Disambiguation ...

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40th international conference on Machine Learning

作者： Qiao, Congyu Xu, Ning Lv, Jiaqi Ren, Yi Geng, Xin Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China RIKEN Ctr Adv Intelligence Project Beijing Peoples R China Zhejianglab Res Ctr Healthcare Data Sci Hangzhou Peoples R China

To reduce annotation difficulty, Partial label learning (PLL) uses ambiguous annotations with candidate labels instead of the exact correct label. It assumes the candidate label set contains the correct label, inducing disambiguation, which is commonly adopted in PLL methods. However, this assumption is impractical as no one could guarantee the existence of the correct label in the candidate label set under real-world scenarios. Therefore, Unreliable Partial Label Learning (UPLL) is investigated where the correct label of each example may not exist in the candidate label set. In this paper, we propose a fusion framework of refinement and disambiguation named FREDIS to handle the UPLL problem. Specifically, with theoretical guarantees, not only does disambiguation move incorrect labels from candidate labels to non-candidate labels but also refinement, an opposite procedure, moves correct labels from non-candidate labels to candidate labels. Besides, we prove that the classifier trained by our framework could eventually approximate the Bayes optimal classifier. Extensive experiments on widely used benchmark datasets validate the effectiveness of our proposed framework. Source code is available at https://***/palm-ml/fredis.

关键词： Machine learning

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Overlapping Entity Relation Extraction Based on Syntactic Dependency Tree and Multi-dimensional Corner Marking Strategy 20th

Overlapping Entity Relation Extraction Based on Syntactic De...

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20th international conference on Intelligent computing (ICIC)

作者： Liu, Xiaojing Li, Canyu Qin, Li Li, Jinliang Wang, Junlei Feng, Zaiwen Mayer, Wolfgang Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China Huazhong Agr Univ Key Lab Smart Farming Agr Animals Wuhan 430070 Peoples R China Huazhong Agr Univ Hubei Engn Technol Res Ctr Agr Big Data Wuhan 430070 Peoples R China Huazhong Agr Univ Minist Educ Engn Res Ctr Intelligent Technol Agr Wuhan 430070 Peoples R China Univ South Australia Ind AI Res Ctr Adelaide SA 4057 Australia

ISBN: (纸本)9789819756681;9789819756698

When extracting entity relations from unstructured Chinese texts, it is necessary to address the problems of relation overlap and entity overlap that may occur within a single sentence. Current mainstream entity relation extraction strategies mostly focus on the influence of syntactic structure information, while ignoring the potential relationships between the syntactic structure of the text and overlapping entity relation triples. A model based on the Syntactic Dependency Tree and Multi-dimensional Corner Marking strategy (SDT-MCM) is proposed for overlapping entity relation extraction in this paper. Firstly, SDT-MCM constructs a character adjacency graph based on its syntactic dependency tree. And graph convolutional neural networks (GCNs) are utilized to extract syntactic features from the character adjacency graph. These are then combined with the semantic features extracted by BERT to form joint features. A novel multidimensional corner marking strategy is introduced to annotate overlap situations. Finally, entities and relations are jointly extracted using a score-based classifier. To validate the effectiveness of SDT-MCM, experiments on five public datasets including DuIE2.0, CMeIE, FinRE, SanWen, and CHIP2022 were conducted for entity relation extraction and overlap entity-relation comparison. SDT-MCM achieved F1 scores of 75.25%, 45.30%, and 36.42% on DuIE2.0, FinRE, and SanWen datasets respectively, outperforming all the compared models.

关键词： joint extraction overlapping entity relation syntactic dependency tree multi-dimensional character tagging strategy

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Weather Forecasting and Analysis Based on Machine Learning 2

Weather Forecasting and Analysis Based on Machine Learning

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2022 2nd international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2022

作者： Chen, Boyang Chen, Erqian Lang, Heran Shenzhen Middle School No.18 Shenzhong Street North Renmin Road Guangdong Province Shenzhen City518000 China School of Information Technology and Electrical Engineering The University of Queensland St LuciaQLD4072 Australia School of Computing Department National university of Singapore 21 Lower Kent Ridge Road 119077 Singapore

ISBN: (纸本)9783800760268

Weather forecasting has become a topic of increasing concern due to unnatural causes such as human activities. Although the weather forecasting methods in the past: traditional synoptic method and numerical prediction method can predict the change of the weather very accurately, but these two methods need to monitor and process a large amount of data, which consumes resources and manpower. When people just want to get the weather of a particular area, it's not worth it. Our study provides a new way to predict weather from small pieces of data. The data includes temperature, rainfall, wind direction, wind speed, humidity, pressure, cloud and sunshine which previous research did not use all of them. Firstly, a small part of data was obtained from reliable databases. Then this paper compared different methods (Drop the incomplete samples, using mean value of the column, K-nearest-neighbors) to select the best method for refining data sets. Then this paper compared different models (Random Forest Regression, Logistic Regression, Support Vector machine) to select the best model suitable for small data for machine learning. Finally, this paper formed tables, bar charts and broken line charts to show the weather prediction and evaluation of each model through data visualization, as well as the prediction of evaporation, rainfall and other future weather data. This study created an interactive web page to showcase the final result and make it easy for people to view at any time. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Weather forecasting

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Application strategy of big data and cloud computing in the Internet of things 2

Application strategy of big data and cloud computing in the ...

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2nd IEEE international conference on Artificial intelligence in Everything, AIE 2022

作者： Li, Mindong Shi, Yuhong Shandong Huanghe River Affairs Bureau Information Center Shandong China

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

The Internet has become a new growth point of the country's future economy. Modern network is built by combining wireless technology, optical fiber transmission technology, computer network technology and computer software. In the future, with the expansion of the scale of sensor networks, the data obtained from sensors will consist of TB and Pb data. They include sensor layer, transport layer, integration layer and application layer. In this process, the combination of big data and cloud brings opportunities and challenges to the network. This paper describes the relevance and characteristics of big data and cloud in Intranet, laying the foundation for more effective network development. © 2022 IEEE.

关键词： big data

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