检索结果-内蒙古大学图书馆

IP SafeGuard - An AI-Driven Malicious IP Detection Framework

IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt19385 Jordan Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

引用

computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

来源：评论

学校读者我要写书评

暂无评论

EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

引用

IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

来源：评论

学校读者我要写书评

暂无评论

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing ...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Al Amin Kamrul Hasan Sharif Ullah M. Shamim Hossain Department of Electrical & Computer Engineering College of Engineering Tennessee State University TN USA Department of Computer Science & Engineering College of Science and Engineering University of Central Arkansas Conway AR USA Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks.

关键词： Data privacy computer vision Image analysis Accuracy Artificial neural networks Transformers Robustness Encryption Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

User Experience (UX) Enrichment Through Digital Branding 1st

User Experience (UX) Enrichment Through Digital Branding

引用

1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Zia, Saira Iqbal, Muhammad Waseem Noor, Misbah Aqeel, Muhammad Hamid, Khalid Department of Computer Science Superior University Lahore Lahore Pakistan Department of Software Engineering Superior University Lahore Lahore Pakistan

ISBN: (纸本)9783031776168

The change in digital marketing from desktop to mobile contexts (e.g., browser) is getting increasingly pronounced (e.g., within mobile applications). Banners and videos, on the other hand, remain mostly unchanged as ad distribution mechanisms. We look at how to adapt ad distribution methods to the mobile environment. We demonstrate how demanding gentle contact with the ad – while lowering available screen space – might improve users’ perceptions of remote marketing. Business remember may be improved by more encounters with the commercial distribution device. A holistic knowledge of customer experience necessitates examining in marketing and branding, user experience is important. This study’s aim is to offer a theoretical background for finer comprehension the implications of user experience (UX), client experience (CX), and corporate reputation (BE) are all terms used to describe how a user, a customer, or a brand (BE) on brand equity (BX). As a mediator with a high path weight, customer experience has a major impact on brand equity. Employing UX techniques that connect with management plans can help companies reach a high level of customer satisfaction and brand value perception by consumers. This study’s findings and analyses can assist business in developing a strategy. For understanding which components of User Experiences are important for brand image and the customer satisfaction. Given the increased financial significance of the "user experience," more gamification should be used. As an outcome, the study’s goal is to look into the causes and the consequences of an online experience, in the area of internet gamification, it also partially mediates the relationship. According to the findings, mobility as it is perceived practical and hedonistic features, as well as perceived mobility characteristics, influence experience design, which in turn drives perceived advantages, perceived value categories, and product equity. This study also confirms the role of use

关键词： Gamification

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Usability Approach of Distributed Database in Health Care System 1st

Usability Approach of Distributed Database in Health Care Sy...

引用

1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Mazhar, Sana Iqbal, Muhammad Waseem Irshad, Muhammad Ahmad Department of Software Engineering Superior University Lahore54000 Pakistan Department of Computer Science Superior University Lahore54000 Pakistan

ISBN: (纸本)9783031776168

The blockchain is safe mechanism for store and distributed data due to its transparency. Every block of the chain serves as both an individual component that has its own data as well as a dependent connection inside the shared network, so this duality provides a networks organized by users who keep and share data instead of third party. Blockchain has several uses in healthcare, including the improvement of applications for mobile health monitoring-devices, the distribution and store of electrical-medical-records, clinical research data, as well as the storage of insurance information. Blockchain analysis throughout insurance is currently limited, but blockchain is at the frontlines of trying to transform the medical system;throughout its distributed concepts, blockchain could even keep improving patient records availability and privacy. This article presents a solution for managing identity and access in a digital system that makes use of blockchain technology to help with entity authentication and permission. Using the Permissioned Blockchain architecture, a prototype illustrates the use of blockchain in access and identity management. It gives a proof-of-concept centered on some use case involving Electronic-Health-Records from the health sector in which an immutable and traceability record for information related to patients is desired. Blockchain has the implicit to meet specific unique criteria, similar as confidentiality and invariability, and so might be employed in a wide range of operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A Convolutional Neural Network Approach for Mood Classification in Short Texts Using Character-Level Details and Local Text Structure 1st

A Convolutional Neural Network Approach for Mood Classificat...

引用

1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Hussain, Moodser Jameel, Muhammad Ullah, Muhammad Farhat Jabbar, Taimoor Hassan Irfan, Roha Iqbal, Muhammad Waseem Department of Information Technology University of the Punjab Gujranwala Campus Gujranwala Pakistan Department of Computer Science Superior University Lahore Lahore Pakistan Department of Software Engineering University of Lahore Lahore Pakistan Department of Computer Science BZU Multan Pakistan Department of Computer Science University of Central Punjab Lahore Lahore Pakistan Department of Software Engineering Superior University Lahore Lahore Pakistan

ISBN: (纸本)9783031776168

Mood classification is a crucial and complex task in the fields of psychology and Natural Language Processing (NLP). It is particularly challenging to assess an individual’s mood based on their chats or comments on social media platforms. This study introduces a Convolutional Neural Network (CNN) for mood classification and evaluates its efficacy on the ISEAR dataset. The dataset comprises 7,652 short texts labeled with seven different moods including joy, anger, fear, guilt, disgust, sadness, and shame. Our proposed model achieves an impressive F1 score of 94.59%, surpassing existing statistical and deep learning models. The superior performance can be attributed to two key features of our approach. Firstly, our model incorporates the local structure of the text by leveraging relationships between adjacent word embeddings, allowing it to capture the subtle nuances of language. Secondly, it captures character-level details within the input text, allowing for a more comprehensive understanding of the data. Overall, our study demonstrates the effectiveness of the CNN-based approach for mood classification in NLP, with potential implications for developing automated systems that can accurately recognize and respond to people's moods in various applications, such as mental health and customer service. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Classification of Academic and Psychological Challenges in Higher Education

AI-Driven Classification of Academic and Psychological Chall...

引用

International Conference on New Trends in Computing sciences (ICTCS)

作者： Seif Hshem Alazzam Mohammad Al-oudat Department of Software Engineering Philadelphia University of Jordan Amman Jordan Department of Computer Science Philadelphia University of Jordan Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Students enrolled in university experience academic along with mental stressors, which negatively impact their academic results. The research implements artificial intelligence methods for classifying and forecasting the difficulties which higher education students encounter within Jordanian universities. An electronic questionnaire was designed through structured procedures and received validation from eight academics before being distributed to 1020 students. Statistical analysis through Statistical Package for the Social sciences (SPSS) validated the questionnaire data while testing the reliability of its findings. Students were classified into four categories—Academic Difficulties and Academic and Psychological Challenges and Psychological Distress alongside Normal—through the utilization of the GPT-4o mini API as a Large Language Model (LLM). Machine learning algorithms were applied to evaluate classification performance. Support Vector Machine (SVM) demonstrated the best result among classification models with an accuracy rate of 88.2% while Logistic Regression came second with 87.7% accuracy. A significant number of 54.7% students faced academic challenges while 60.8% of students reported psychological issues. The generated results will assist educational institutions by guiding their early prevention programs along with choosing appropriate assistance methods to enhance educational outcomes.

关键词： Support vector machines Accuracy Statistical analysis Large language models Computational modeling Education Psychology Nearest neighbor methods Reliability Testing

来源：评论

学校读者我要写书评

暂无评论

Revisiting kernel attention with correlated Gaussian process representation 24

Revisiting kernel attention with correlated Gaussian process...

引用

Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Long Minh Bui Tho Tran Huu Duy Dinh Tan Minh Nguyen Trong Nghia Hoang FPT Software AI Center and School of Electrical Engineering and Computer Science Washington State University FPT Software AI Center and Department of Mathematics National University of Singapore FPT Software AI Center Department of Mathematics National University of Singapore School of Electrical Engineering and Computer Science Washington State University

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP's kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：