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Implementation Graph database Framework for Credit Card Fraud Detection 8

Implementation Graph Database Framework for Credit Card Frau...

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8th international conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Mauliddiah, Nurrahmi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth'\ Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. the data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. this is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%. © 2023 the Authors. Published by Elsevier B.V.

关键词： Alert Credit card fraud detection Dashboard TigerGraph

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Lotnet: A Low Overhead Approach for Cellular Traffic Prediction of Large-Scale Urban Area

Lotnet: A Low Overhead Approach for Cellular Traffic Predict...

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IEEE international conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Dongliang Li Haozhen Li Hui Zhang Boyuan Zhang Xinyu Gu Beijing University of Posts and Telecommunications Beijing China Shandong Inspur Database Technology Co. Ltd Jinan China Purple Mountain Laboratories Nanjing China

With the continuous development of mobile communication technology and the arrival of the 5G era, the demand for mobile data traffic is increasing. Accurate traffic prediction is of great significance in meeting users' data transmission needs and optimizing network resource allocation. However, due to the uneven distribution of cellular network traffic in large-scale urban area, the current cellular traffic prediction methods based on deep learning suffer from performance degradation as well as large storage overhead. therefore, we design a Low Overhead wireless Traffic prediction Network (LOTNet) for performance enhancement. Firstly, to improve prediction performance under the large-scale city area, the Context Embedding and Multi-Scale Spatiotemporal Expression Long Short Term Memory (CMS-LSTM) structure and the dual attention mechanism are used in the prediction module for the extraction of spatiotemporal features. Secondly, to reduce the storage overhead caused by large-scale prediction, we use the autoencoder structure for hotspot cells extracting. the experiments on real cellular traffic data proves the effectiveness of our scheme.

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Proceedings - 8th international conference on Applied Computing and Information technology, ACIT 2021

Proceedings - 8th International Conference on Applied Comput...

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8th international conference on Applied Computing and Information technology, ACIT 2021

ISBN: (纸本)9781450384933

the proceedings contain 24 papers. the topics discussed include: estimation of emotional scenes from lifelog videos with moving average of facial expression intensity;facial expression intensity estimation using deep convolutional neural network;fuzzy database and interface to analyze management system operations;materialized view driven architecture over lattice of cuboids in data warehouse;identification of corresponding numerical attributes in heterogeneous databases based on instances;reducing the repairing penalty on misspeculation in thread-level speculation;GPU parallelization of all-pairs-shortest-path algorithm in low-degree unweighted regular graph;an adaptive tiering scheme for an autonomous distributed storage system;and redundancy of routing information on the distributed key-value store based on order preserving linear hashing and skip graph with the load balancing method.

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Robust WI-FI Enabled Human Activity Recognition Via Unsupervised Adversarial Domain Adaptation

Robust WI-FI Enabled Human Activity Recognition Via Unsuperv...

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IEEE international conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Jiahui Chen Haozhen Li Lei Li Boyuan Zhang Xinyu Gu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Shandong Inspur Database Technology Co. Ltd Jinan China Purple Mountain Laboratories Nanjing China

Human action recognition using Wi - Fi signals can significantly facilitate the development of human-computer interaction applications in a wide range of applications. A key challenge of existing research is constructing human action recognition models that can maintain stable performance under dynamic spatio-temporal conditions. In this paper, we propose an unsupervised adversarial domain adaptation model WiADG Pro, which can effectively capture the temporal features in the data. Experimental results on Widar 3.0 show that our proposed model can greatly improve the performance of the model in the domain adaptation experimental phase. And due to the unequal amount of data in the two domains, we also explore in depth the effects of different data amounts in the source and target domains and actionindependent domain information on the model performance.

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Towards Robust Autonomous Vehicle Software: extending SAM for a Holistic Security Analysis

Towards Robust Autonomous Vehicle Software: Extending SAM fo...

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international conference on System Reliability and Science (ICSRS)

作者： Alexander Fischer Ramin Tavakoli Kolagari Faculty of Computer Science Nuremberg Institute of Technology Bavaria Germany

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

the Security Abstraction Model (SAM) is a meta-model that enables comprehensive security modeling and does not only consider technological aspects, but also takes into consideration the influence of social engineering. this plays an important role by exploiting vulnerabilities in human behavior and thus represents a dimension that must not be neglected in possible attacks on vehicle systems. this work is dedicated to the advancement of the Security Abstraction Model (SAM) with the aim of enabling improved modelling and analysis of vulnerabilities in the context of software and system security; the relevant standards and best practices of the international security literature for embedded systems are harmonized in one metamodel. By extending SAM, this research enables a more nuanced understanding and representation of vulnerabilities, attacks and scenarios, improving the effectiveness of security analysis and remediation strategies. the integration of different perspectives, including technological, social and behavioral factors, underlines the comprehensive nature of the proposed model. this interdisciplinary approach recognizes the complexity of security threats in autonomous vehicle systems and enables stakeholders to develop more effective countermeasures. this work presents an overview of the extension of the Security Abstration Model (SAM) and evaluates this security technology through expert interviews. the results demonstrate that our approach is compliant not only in the automotive domain, but also with standards for embedded systems from other domains. It thus provides a comprehensive and universal approach for the development of secure systems.

关键词： Analytical models Embedded systems Software Security Stakeholders Interviews Standards Autonomous vehicles Automotive engineering Context modeling

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A database File Storage Optimization Strategy Based on High-Relevance Mode Access Data Compression 8th

A Database File Storage Optimization Strategy Based on High...

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8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Gao, Rui Lu, Yixuan Liu, Jian Yu, Jun Tian, Weiguo Du, Haiwen Kang, Chuanmeng Yin, Weiqi Zhu, Dongjie Shanghai J.Y-JZ Network Technology Service Co. Ltd. Shanghai200233 China School of Computer Science and Technology Harbin Institute of Technology Weihai264209 China Co. Ltd. Shanghai200065 China School of Astronautics Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783031067600

With the improvement of social informatization and the popularization of Internet of things devices, the scale, complexity and diversity of data are currently growing rapidly, and traditional storage solutions have been unable to meet the complex and diverse applications and large-scale new storage requirements. Existing storage solutions still have deficiencies in data compression and adapting to the diversity of system architectures, resulting in a large waste of storage space resources, which in turn increases the total cost of ownership of platform data. therefore, this paper will study the data compression strategy of database file storage, and propose a high-relevance mode access data compression method. the data request of the write-only instance of the database hosted on the cloud platform is aggregated with the system workload. the data stored in the write-only instance is compressed, which improves data storage efficiency and storage space utilization. the method was validated using data in real enterprise scenarios. the experimental results show that the proposed method has a certain degree of improvement in storage space utilization compared with the original method. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data compression

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Normalization Framework for Vulnerability Risk Management in Cloud 8

Normalization Framework for Vulnerability Risk Management in...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Ahmadi, Vida Arlos, Patrik Casalicchio, Emiliano Blekinge Institute of Technology City Network International AB Dept. of Computer Science Karlskrona Sweden Blekinge Institute of Technology Dept. of Computer Science Karlskrona Sweden Dept. of Computer Science Sapienza University of Rome Italy

ISBN: (纸本)9781665425742

Vulnerability Risk Management (VRM) is a critical element in cloud security that directly impacts cloud providers' security assurance levels. Today, VRM is a challenging process because the dramatic increase of known vulnerabilities (+26% in the last five years), and because it is even more dependent on the organization's context. Moreover, the vulnerability's severity score depends on the Vulnerability database (VD) selected as a reference in VRM. All these factors introduce a new challenge for security specialists in evaluating and patching the vulnerabilities. this study provides a framework to improve the classification and evaluation phases in vulnerability risk management while using multiple vulnerability databases as a reference. Our solution normalizes the severity score of each vulnerability based on the selected security assurance level. the results of our study highlighted the role of the vulnerability databases in patch prioritization, showing the advantage of using multiple VDs. © 2021 IEEE.

关键词： Risk assessment

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Training CSI Feedback Model with Federated Learning in Massive Mimo Systems

Training CSI Feedback Model with Federated Learning in Massi...

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IEEE international conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Zhilin Du Haozhen Li Lei Li Boyuan Zhang Zhenyu Liu Xinyu Gu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Shandong Inspur Database Technology Co. Ltd Jinan China Purple Mountain Laboratories Nanjing China

the basic centralized learning method in Deep learning-based downlink channel state information (CSI) feedback requires a large dataset that needs to be collected either in advance with huge time and workforce costs or by distributed user equipment (UE) through wireless communication links producing huge communication overhead. Federated learning (FL)-based training can reduce the burden of CSI data collection, but the requirement of iterative model information transmission over wireless channels still leads to high communication overhead. To overcome this communication bottleneck, we develop a novel communication-efficient FL algorithm named FedDec, which only needs the local decoders to participate in model aggregation and allows each UE to upload only part of the decoder parameters by a client grouping method. Simulation results support the effectiveness of FL in CSI feedback and illustrate that FedDec can reduce the communication overhead by more than 50% compared with the widely-accepted FL algorithms, which require all model parameters to participate in model aggregation.

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Monitoring and Alarming Activity of Islamic Prayer (Salat) Posture Using Image Processing 8

Monitoring and Alarming Activity of Islamic Prayer (Salat) P...

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8th international conference on Computer and Communication Engineering, ICCCE 2021

作者： Rahman, Md Mozasser Alharazi, Rayan Abbas Ahmed Badri, Muhammad Khairul Imran B Zainal Universiti Tun Hussein Onn Malaysia Department of Mechanical Engineering Technology Muar Malaysia International Islamic University Malaysia Kulliyyah of Engineering Department of Mechatronics Kuala Malaysia

ISBN: (纸本)9781728110646

this paper introduced a Salat Inspection and Training System based on Machine Vision and Image Processing Subject. In Islam, prayer (i.e., Salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures' perspective, there are predefined human postures that must be performed in a precise manner. there are lots of materials on the Internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer's postures. Many features of images are being extracted and analyzed. Methods for image comparison and pattern matching are used to study the system's effectiveness by using several combining algorithms, such as Euclidean Distance, Template Matching and Grey-Level Correlation, to compare the images of the user and the database. the experiments' results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat. Limitations of the system, such as lighting, is discussed regarding how it affects the system's performance. © 2021 IEEE.

关键词： Template matching

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Fuzzy database and Interface to Analyze Management System Operations 21

Fuzzy Database and Interface to Analyze Management System Op...

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8th international conference on Applied Computing and Information technology, ACIT 2021

作者： Shiono, Yasunori Goto, Takaaki Yoshizumi, Toshihiro Tsuchida, Kensei Yokohama National University Yokohama-shi Kanagawa Japan Kaetsu University Kodaira-shi Tokyo Japan Toyo University Kawagoe-shi Saitama Japan

ISBN: (纸本)9781450384933

Management systems, such as Information Security Management Systems (ISMS) and IT Service Management Systems (ITSMS), are typically applied to the entire organization, and such systems attempt to provide continual improvement. Efficient engagement with business operations is an issue;consequently, understanding and analyzing actual situations is required. We have been researching analysis methods using fuzzy theory, which enables imprecise expressions. By building a useful fuzzy database and realizing an analysis method for management system operations, our goal is to develop a useful tool for organizational management. In this paper, we introduce the concept of fuzzy database construction, an analysis method, and a case study related to management system operations. © 2021 ACM.

关键词： database systems

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