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共 108025 条记录，以下是4241-4250 订阅

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Enhancing Credit Card Fraud Detection with Deep learning and Graph Neural Networks 15

Enhancing Credit Card Fraud Detection with Deep Learning and...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sherwin Akshay, J.G. Vinusha, T. Sharon Bianca, R. Sarath Krishna, C.K. Radhika, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Financial fraud poses a major threat to financial service institutions and clients, facilitating anomaly detection capabilities. This paper dives into deep learning models which can be implemented to detect and distinguish fraudulent transactions from normal transactions and flag anomalies. Further, conversion of tabular data to graphs have been implemented for keen understanding and improved pattern analysis of fraudulent transactions, challenges include evolving attack strategies, class imbalance and model interpretability. The proposed methods utilize a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like Auto-Encoders and Recurrent Neural Networks. In the graphical representation, this study focuses on implementing Louvain Modularity, Girvan-Newman and Label Propagation Algorithm for effectiveness in community detection. The outcomes exhibit consistently reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security. © 2024 IEEE.

关键词： Auto-Encoders CatBoost GAN GNN GRU RNN Unsupervised learning XG-Boost

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Detecting DDoS Attacks in Software-Defined Networking through the Utilization of Supervised and Unsupervised learning Techniques 15

Detecting DDoS Attacks in Software-Defined Networking throug...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sathwik, T. Reddy, Tadi Sai Vishruth Gangavarapu, Venkat Rohith Bhaskaran, Sreebha Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

Given the dynamic nature of SDN environments, it is necessary to quickly recognize Distributed Denial of Service (DDoS) attacks to maintain our network’s integrity and ensure uninterrupted service delivery. However, the centralized control plane of SDN makes it particularly vulnerable, as a compromise here can allow attackers to manipulate network traffic and configurations extensively. The traditional detection methods often have trouble keeping up with these attacks’ dynamic nature, resulting in high false positive rates and poor response. This paper proposes an approach that uses supervised and unsupervised learning techniques to enhance DDoS attack detection capabilities in SDN infrastructures. Using Mininet and Ryu, our method involves collecting network traffic data, extracting relevant features, and constructing classification models based on labelled datasets that can identify typical attack patterns. Additionally, these initiatives are supported by unsupervised learning approaches like clustering or anomaly detection, which help reveal new types of attack signatures not seen before and abnormal behaviours within networks caused by them. As a result, what we have done here enables real-time detection against DDoS through seamless integration into the Ryu controller, thus strengthening the security posture against emerging threats in cyberspace targeting SDNs. ©2024 IEEE.

关键词： Self-supervised learning

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Wireless ECG Monitoring and Automated Heart Diagnosis: A Mobile App Solution Using Deep learning 15

Wireless ECG Monitoring and Automated Heart Diagnosis: A Mob...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sunil, M. Dhanush, Uppalapati Sai, Thippaluru Tharun Jyotsna, C. Sanjay, Thokali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

This research presents a novel solution to the serious global health issue of cardiovascular diseases. The suggested technology revolutionizes remote cardiac monitoring by combining wireless Electrocardiogram sensors and powerful deep learning algorithms. A user-friendly mobile application acts as a center for data collection, storage, and analysis in real time. More importantly, a convolutional neural network can be used for the automatic detection of cardiac problems by pattern extraction from Electrocardiogram readings. The system enables immediate action by sending real-time notifications for detected anomalies, while also keeping a detailed historical record for long-term monitoring. Prioritizing data security, messages are encrypted, and the system contains user authentication mechanisms that are in line with healthcare standards. Engaging individuals in proactive health management, this proposal aims to foster cooperation between users and healthcare providers for better patient outcomes. The suggested system shall offer accessibility, accuracy, and user-centricity, coupled with a new approach toward solving the global cardiovascular disease crisis using wireless Electrocardiogram monitoring and deep learning technology. ©2024 IEEE.

关键词： cardiovascular diseases Convolutional Neural Network Wireless Electrocardiogram Monitoring

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Adversarial Attack Optimization and Evaluation for Machine learning-Based Dark Web Traffic Analysis 33rd

Adversarial Attack Optimization and Evaluation for Machine L...

引用

33rd international conference on Software and Data engineering

作者： Harrison, Nyzaireyus Broome, Heather Shrestha, Yaju Robles, Alexander Gautam, Aayush Rahimi, Nick Univ Southern Mississippi Hattiesburg MS 39402 USA

ISBN: (纸本)9783031752001;9783031752018

Machine learning (ML) is quickly becoming one of the most transformative technologies in the field of computing. Applications of MLare wide-spread and growing exponentially, revolutionizing the future of major industries such as finance, healthcare, automotives, and more. This has made it more necessary than ever to recognize the instability created by adversarial attacks-the deliberate manipulation of data to mislead ML models. This instability must be addressed through researching the effects of adversarial attacks and how they can be better recognized. Our research explored the use of adversarial attacks in dark web network traffic analysis by first improving our understanding of how adversarial attacks could be optimized. We manipulated a dataset of dark web traffic data through the analysis of confusion matrices and Euclidean distances, aiming to cause maximum confusion for each of our models. We then trained and tested each model in a variety of scenarios to further our understanding of weaknesses in both the traffic data and the machine learning techniques employed.

关键词： Dark web Machine learning Adversarial attacks Network traffic analysis

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Application of Decision Tree Algorithm in teaching Quality Analysis of Physical Education 2nd

Application of Decision Tree Algorithm in Teaching Quality A...

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2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Zhang, Chengliang Yang, Hui Qujing Medical College Yunnan Qujing655000 China

ISBN: (纸本)9783031239496

learning situation analysis is not only the basic link of teaching activities, but also one of the basic ways to improve effective teaching. However, only on the premise of ensuring the effectiveness of learning situation analysis can we improve the effectiveness of teaching. Compared with other teaching, physical education teaching has more urgent needs and stricter requirements for learning situation analysis. Therefore, this paper uses data analysis technology to elaborate what teachers should pay attention to and how to do when doing learning situation analysis of physical education teaching from the current situation of learning situation analysis of physical education teaching, the mode of learning situation analysis of physical education teaching and the method of learning situation analysis of physical education teaching, In order to provide theoretical guidance for physical education teaching practice. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Data handling

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REINFORCEMENT learning FOR OPTIMAL KICKING ACTIONS IN HUMANOID ROBOTICS: ADVANCING ROBOTIC AUTONOMY AND VERSATILITY 45

REINFORCEMENT LEARNING FOR OPTIMAL KICKING ACTIONS IN HUMANO...

引用

2024 international Annual conference of the American Society for engineering Management and 45th Annual Meeting: engineering Management Riding the Waves of Smart Systems, ASEM 2024

作者： Dodda, Suresh Chintala, Sathish Kumar Mallreddy, Sukender Reddy Macha, Sharath Chandra Vasa, Yashwanth Bonala, Sapan Bharadwaj Kamuni, Navin Alla, Sujatha Old Dominion University United States

ISBN: (纸本)9798985333480

Acquiring the necessary skills to perform a work effectively and efficiently requires a significant investment of time and computing power. Previous applications of Reinforcement learning (RL) for action optimization in humanoid robotics have shown how promising this technology is for moving robotics towards true autonomy and versatility. Therefore, this study offers the first use of RL to create an entirely optimal kicking action for the Alderbaran Nao robot. Kicking motions that were steady, precise, quick, and able to kick farther than any existing RoboCup squad were generated by optimizing for a multi-objective reward function. We demonstrate that the ideal kicking motions can be modified to produce angled kicks by putting a dynamic kicking module into practice. We also research on various kicking movements and more intricate search spaces that can benefit from the methodology presented in this study. Copyright © American Society for engineering Management, 2024.

关键词： Reinforcement learning

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Fraud Detection in Financial Transactions: A Machine learning Approach 9

Fraud Detection in Financial Transactions: A Machine Learnin...

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9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350365092

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector. © 2024 IEEE.

关键词： Logistic regression

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Development of E-learning Software for Aluminum Alloy Bending Experiment Based on Simulation Technology 5

Development of E-learning Software for Aluminum Alloy Bendin...

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5th international conference on Computer Science, engineering, and Education, CSEE 2024

作者： Wang, Shang Mao, Xing Zhang, Yajuan Beijing Polytechnic School of Automotive Engineering Beijing China University of Science and Technology Beijing Institute of Engineering Technology Beijing China Beijing Polytechnic College of Automation Engineering Beijing China

ISBN: (纸本)9798350376852

This study developed a simulation-based E-learning software to enhance the learning outcomes of aluminum alloy bending experiments for engineering mechanics students. The bending process was modeled using simulation software on a server infrastructure. Mises stress contour plots generated from the simulations were incorporated into an E-learning application developed using C# for educational purposes. The findings demonstrate the software's effective impact on improving comprehension. Visual materials such as stress distribution diagrams significantly enhanced learners' understanding of abstract concepts. The software also led to notable increases in learner enthusiasm and interest in the subject. Post-experiment surveys showed 89.47% and 94.87% acceptance rates respectively, indicating the value of supplementing traditional teaching with digital resources. Overall, incorporating simulation technologies addresses scarcity of such materials and warrants further exploration. This research emphasizes the benefits of integrating simulation-based E-learning in mechanics education and lays the foundation for optimizing future educational experiences through innovative digital-focused approaches. © 2024 IEEE.

关键词： E-learning

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JUmPER: Performance Data Monitoring, Instrumentation and Visualization for Jupyter Notebooks

JUmPER: Performance Data Monitoring, Instrumentation and Vis...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Werner, Elias Rygin, Anton Gocht-Zech, Andreas Dobel, Sebastian Lieber, Matthias TU Dresden ScaDS.AI Dresden Leipzig CIDS Dresden Germany German Environment Agency AI Lab Leipzig Germany TU Dresden ZIH CIDS Dresden Germany

ISBN: (纸本)9798350355543

Computational performance, e.g. CPU or GPU utilization, is crucial for analyzing machine learning (ML) applications and their resource-efficient deployment. However, the ML community often lacks accessible tools for holistic performance engineering, especially during exploratory programming such as implemented by Jupyter. Therefore, we present JUmPER, a Jupyter kernel that supports coarse-grained performance monitoring and fine-grained analysis tasks of user code in Jupyter. JUmPER collects system metrics and stores them alongside executed user code. Built-in Jupyter magic commands provide visualizations of the monitored performance data directly in Jupyter. Additionally, code instrumentation can be enabled to collect performance events using Score-P. JUmPER preserves the exploratory programming experience by seamlessly integrating with Jupyter and reducing kernel runtime overhead through in-memory (pipe) communication and parallel marshalling of Python's interpreter state for the Score-P execution. JUmPER thus provides a low-hurdle infrastructure for performance engineering in Jupyter and supports resource-efficient ML applications. © 2024 IEEE.

关键词： Jupyter Machine learning Performance engineering Resource efficiency

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Intelligent Energy Management Energy Storage Systems Using Machine learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 international conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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