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Analysis of Cyber Security Threats in Payment Gateway technology 3

Analysis of Cyber Security Threats in Payment Gateway Techno...

引用

3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sharma, Hari Prapan Krishna, Somanchi Hari Prakash, R Vijay Tiwari, Mohit Tiwari, Tripti Kumar, Madarapu Naresh GLA University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Aushapur Village Ghatkrsae Mandal Malkajgiri Medchal Dist501301 India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A 4 Rothak Road Paschim Vihar Delhi India BVIMR Department of Management Studies A 4 Rothak Road Paschim Vihar Delhi India National Informatics Centre Ministry of Electronics and Information Technology Delhi India

ISBN: (纸本)9798350399264

There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenience for online shoppers. Cyber-attack techniques, on the other hand, are developing at the same rapid pace as security mechanisms. The authors of this paper examine the history of electronic payment systems, from the development of the corresponding language through the emergence of today's standard electronic payment systems. It also reveals a lack of security measures and approaches to fixing the problem. The current survey research makes a significant contribution by outlining the state of the electronic payment system framework and the possibilities it presents for the development of e-commerce in the future. The rates of suspicious purchases, which will serve as a yardstick in the creation of a trustworthy e-payment system, have been briefly discussed and analyzed. © 2023 IEEE.

关键词： Cybersecurity

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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AutoRoto: Automated Rotoscoping with Refined Deep Masks

AutoRoto: Automated Rotoscoping with Refined Deep Masks

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2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Dissanayake, Sajitha Ayoob, Mohamed Vekneswaran, Prathieshna University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9781665425032

The legendary movie Jurassic Park (1993) would have been nearly impossible without computer Generated Imaginary(CGI). The movie's ground-breaking visual effects was able to smoothly integrate virtual characters with real-life footage. This rapidly increased the use of visual effects in modern movies. Creating high-end visual effects is a complex workflow where each shot is subjected to artistic operations. The key step in this workflow is the process called digital rotoscoping. It is the process of manually outlining and cutting out the foreground characters in a raw video frame via software. This is a time-consuming process and the average number of frames a professional artist can accomplished per day is 15. Due to this, it has become a temporal bottleneck of the VFX *** order to address the above mentioned issue, an automated rotoscoping approach with instance segmentation is presented in this paper. The novel method is efficient, accurate and will redeem the time of rotoscoping artists. Our model was able to extract foregrounds of multiple instances of classes and to the best of our knowledge our rotoscoping tool is the most generalized to extract foregrounds. © 2021 IEEE.

关键词： Object detection

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An Hybrid Modelling for Optimal Utilisation on Cloud Resources using Machine Learning Algorithm

An Hybrid Modelling for Optimal Utilisation on Cloud Resourc...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Atyam, Nageswara Rao Babu P., Ramesh Nayak, Nihar Ranjan Saikia, Utpal Ananda Kumar, K.S. Aravindharamanan, S. Cmr Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India Wollega University Department of Computer Science College of Engineering Technology Nekemte Ethiopia Department of Mca Andhra Pradesh Chittoor India Silapathar College Department of Mathematics Silapathar Assam Dhemaji India School of Computing and Informatics College of Engineering and Technology Dilla University Snnpr Dilla Ethiopia Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665484251

The great majority of data centres are now underutilised, which is a concern. It is estimated that they are only used at 15% of their maximum capacity the great majority of the time. As a result of significant increases in server demand, additional space in these data centres has been added. This study presents the development of a hybrid paradigm for the efficient utilisation of cloud resources. Cloud resource provisioning is made more efficient through the use of machine learning and particle swarm optimization techniques. In order to determine if the model performs as intended, CloudSim is used to simulate the model's performance, and the results are compared against a range of performance measurements, such as the make span, delay, and throughput. © 2022 IEEE.

关键词： Machine learning

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Security Revisited: Honeytokens meet Google Authenticator 7

Security Revisited: Honeytokens meet Google Authenticator

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Papaspirou, Vassilis Papathanasaki, Maria Maglaras, Leandros Kantzavelou, Ioanna Douligeris, Christos Ferrag, Mohamed Amine Janicke, Helge University of West Attica Department of Informatics and Computer Engineering Athens Greece University of Thessaly Department of Cs and Telecommunications Lamia Greece Cyber Technology Institute De Montfort University Leicester United Kingdom University of Piraeus Department of Informatics Piraeus Greece Guelma University Department of Computer Science Guelma Algeria Cyber Security CRC Edith Cowan University Perth Perth6027 Australia

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security. © 2022 IEEE.

关键词： Authentication

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-b...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Das, Supantha Hazra, Arnab Pati, Soumen Kumar Ghosh, Soumadip Mallik, Saurav Banerjee, Suharta Mukherji, Ayan Li, Aimin Zhao, Zhongming Academy of Technology Hooghly Department of Information Technology West Bengal India Future Institute of Technology Kolkata Department of Computer Science and Engineering West Bengal India Maulana Abul Kalam Azad University of Technology West Bengal Haringhata Department of Bioinformatics West Bengal Nadia India Sister Nivedita University Department of Computer Science and Engineering West BengalNew Town Kolkata India Harvard T H Chan School of Public Health Department of Environmental Health BostonMA United States Meghnad Saha of Institute of Technology Department of Computer Science and Engineering West Bengal Kolkata India Pailan College of Management & Technology Department of Computer Science and Engineering West Bengal Kolkata India Xi'an University of Technology School of Computer Science & Engineering Shaanxi Xi'an710048 China The University of Texas Health Science Center Center for Precision Health School of Biomedical Informatics HoustonTX77030 United States

ISBN: (纸本)9781665468190

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers. © 2022 IEEE.

关键词： Decision trees

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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IEEE International Conference on Big Data

作者： Piotr Biernacki Adam Ziebinski Jia-Hao Syu Jerry Chun-Wei Lin Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors.

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Exploration of the Role of Cryptoprocessors in Advancing IoT Security

Exploration of the Role of Cryptoprocessors in Advancing IoT...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Elena Kakoulli Eleftherios Zacharioudakis Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Igor Sikorsky Kyiv Polytechnic Institute National Technical University of Ukraine Kyiv Ukraine

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This paper presents a thorough exploration of Cryptoprocessors in the context of the Internet of Things (IoT). The focus is on hardware support, cryptographic processors, and energy-efficient designs to fortify the security of IoT ecosystems. Leveraging insights from recent research papers, the survey examines various aspects of Cryptoprocessor implementations, ranging from lightweight countermeasures to advanced lattice-based post-quantum cryptography. The proposed paper amalgamates findings from studies on hardware architectures, energy-efficient designs, and innovative cryptographic algorithms to provide a holistic overview of state-of-the-art advancements in IoT security. The synthesis of this survey’s exploration aims to contribute to the ongoing discourse on securing IoT devices through robust hardware-backed cryptographic solutions.

关键词： Surveys Industries Technological innovation Program processors Ecosystems Hardware Energy efficiency

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Integrating Simulation and Optimization: A Case Study in Pamplona for Self-Collection Delivery Points Network Design 35

Integrating Simulation and Optimization: A Case Study in Pam...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Izco, Irene Serrano-Hernandez, Adrian Sawik, Bartosz Faulin, Javier GILT-OR Group Institute of Smart Cities Dept. Statitistics Computer Science and Mathematics Public University of Navarra Campus Arrosadia s/n Pamplona31006 Spain Department of Business Informatics and Engineering Management AGH University of Science and Technology Mickiewicza 30 Kraków30059 Poland

ISBN: (纸本)9788885741881

The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an e-constraint method, where e is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver. © 2023 The Authors.

关键词： Complex networks

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