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A Traceable Privacy-Preserving Transaction Protocol With Evolutionary Threshold Authentication

IEEE Internet of Things Journal 2025年第12期12卷 20787-20799页

作者： Xie, Ninghai Li, Jiguo Lin, Chao Zhang, Yichen Shen, Jian Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Guangzhou University Guangdong Key Laboratory of Blockchain Security Guangzhou510006 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Decentralized payment systems, such as Bitcoin, enable immutable and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this paper, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation. © 2014 IEEE.

关键词： Authentication Protocol

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Towards a User-Friendly Web Application Firewall 11

Towards a User-Friendly Web Application Firewall

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Omar, Fatima Ahmed, Dalia Elnakib, Omar Ahmed, Mohamed Farhan, Nada Hindy, Hanan Abdel-Hamid, Mahmoud Badawi, Yehia Ain Shams University Faculty of Computer and Information Science Cyber Security Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Computer Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Cyber Security Department Giza Egypt Ain Shams University Faculty of Computer and Information Science Computer Science Department Cairo Egypt

ISBN: (纸本)9798350322101

Web Application Firewall (WAF) systems play a vital role in protecting web applications from various security vulnerabilities. However, existing WAF solutions are often costly and complex to configure, limiting their accessibility to smaller organizations. This paper aims to develop an affordable and user-friendly WAF framework that mitigates the most common security vulnerabilities identified in the Open Web Application security Project (OWASP) Top 10. The proposed WAF framework provides protection against attacks such as Cross-Site Scripting (XSS), Structured Query Language Injection (SQLi), Operating System (OS) Command Injection, File Upload, Denial of Service (DoS), Cross Site Request Forgery (CSRF), Path Traversal, and XML External Entity (XXE) attacks. The framework offers an easy-to-use interface with features for easy configuration of mitigation settings. Users can monitor web traffic and receive real-time alerts to quickly identify and respond to potential security threats. Proxy techniques are used to intercept and handle user requests, sanitizing them using the mitigation techniques to prevent the inclusion of malicious code or unauthorized data. Additionally, two machine learning models were implemented. The first model detects malicious requests, and the second model classifies attacks based on their type. Mitigation techniques are employed to filter requests and prevent specific attack types. Regular expressions (Regex) are used to remove malicious code related to XSS, SQL injection, OS Command Injection, File Upload, CSRF, XXE, and Path Traversal attacks. Rate limiting is implemented to mitigate DoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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Password Strength Classification Using Machine Learning Methods 7

Password Strength Classification Using Machine Learning Meth...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Rehman, Habiba Ahmed, Adnan Hussain, Athar Umar, Muhamamd Siraj, Mohammad Ahmed, Waqas Muhamnad, Mir Rehman, Eesha Quaid-e-Awam University of Engineering Science and Technology Department of Computer Science Nawabshah Pakistan Quaid-e-Awam University of Engineering Science and Technology Department of Cyber Security Nawabshah Pakistan College of Engineering King Saud University Electrical Engineering Department Riyadh11421 Saudi Arabia

ISBN: (纸本)9798331534271

Passwords remain a critical component of authentication systems due to their ease of implementation, despite the availability of more secure methods such as biometrics and smart cards. However, password-based systems are vulnerable to various attacks due to the predictable patterns users often employ. This vulnerability necessitates the development of robust strategies to enforce strong passwords. This research focuses on modeling password strength prediction as a classification task using multiple supervised machine learning algorithms. The goal is to classify passwords into categories of weak, medium, and strong, thereby enhancing the security of systems against online and offline attacks. The result findings demonstrate that Artificial Neural Networks (ANN) and Random Forest (RF) significantly outperform their counterparts in predicting password strength, achieving accuracy rates up to 98.97%. This study aims to provide insights into effective password management and underscore the potential of machine learning in enhancing cybersecurity. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Advancements in Operational Transconductance Amplifiers (OTAs) for Design, Applications & Performance Optimization Techniques with Hardware Implementation 3

Advancements in Operational Transconductance Amplifiers (OTA...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Pramath, V. Pavithra, G. Ninawe, Swapnil S. Manjunath, T.C. Bengaluru India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Rajarajeswari College of Engineering Department of Computer Science & Engineering IoT Cyber Security & Block Chain Technology Karnataka Bangalore India

ISBN: (纸本)9798350374643

This research paper presents the design and simulation of a novel architecture for Multiple Path Fully Differential Operational Transconductance Amplifiers (OTAs) with a Dual Flipped Voltage Follower, specifically developed for VLSI-based signal processing applications. Utilizing advanced 50nm CMOS technology and Cadence tools, the proposed OTA architecture achieves enhanced performance and versatility in handling various signal processing tasks. Design employs multiple signal paths, which contribute to improvements in bandwidth, gain, and linearity, while also effectively minimizing common-mode noise. This multi-path approach ensures better signal integrity and reliability in high-performance applications. The integration of the Dual Flipped Voltage Follower further strengthens the OTA's stability and dynamic range, allowing it to operate seamlessly in high-speed VLSI systems. The architecture is designed to cater to the rigorous demands of modern signal processing, where precision and speed are critical. Extensive simulation studies were conducted to validate the OTA design, and the results demonstrate that the proposed approach meets or exceeds the stringent requirements of today's signal processing applications. Findings of this research contribute to the ongoing advancements in VLSI design methodologies and provide a promising solution for engineers working with nanoscale CMOS technologies. Simulation results using Cadence tools highlights effectiveness and robustness of proposed design. © 2024 IEEE.

关键词： CMOS integrated circuits

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Sustainable Energy Provision in Wireless Sensor Networks: An Overview Leveraging Mobile Sink Strategies 21

Sustainable Energy Provision in Wireless Sensor Networks: An...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Lateef, Hadeel Majid Al-Qurabat, Ali Kadhum M. College of Sciences Al-Mustaqbal University Department of Cyber Security Babylon51001 Iraq College of Science for Women University of Babylon Department of Computer Science Babylon Iraq

ISBN: (纸本)9798350374131

Wireless sensor networks (WSNs) can effectively address the hot-spot or sink-hole problem caused by multi-hop routing with the static sink by data collecting using mobile sinks (MS). The optimal path's design, however, is a well-known NP-hard problem. Data transmission from source nodes to base station that is both successful and efficient while reducing energy consumption and data loss is what determines the architecture's overall performance. In WSNs, data collection is done via mobile sinks or static sinks (SS). However, the effectiveness of MS based data collection techniques is higher than that of static sink-based approaches. Nevertheless, MS-based data collection methods have several drawbacks and limitations. Designing a trajectory is therefore an NP-hard task. In this work, we have developed a survey that utilizes path optimization techniques. We also have an overview of different approaches for using SS and MS-based techniques to collect data from a sensor network, as well as different kinds of data collection using MS and some of the difficulties it encounters. Lastly, we have suggested a level-based categorization of the various trajectory techniques that were employed to gather the data. We divided schemes into two categories at the first level: static and dynamic. © 2024 IEEE.

关键词： Motion planning

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IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

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computers, Materials & Continua 2023年第3期74卷 6867-6878页

作者： Waleed T.Al-Sit Nidal A.Al-Dmour Taher M.Ghazal Ghassan F.Issa Department of Computer Engineering Mutah UniversityAl-KarakJordanHigher Colleges of TechnologyDubaiUAE Department of Computer Engineering College of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKM43600BangiSelangorMalaysia

In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective and smarter healthcare *** has the potential to shape the future of clinical research in the healthcare *** sensors,patients,healthcare providers,and caregivers can connect through an IoMT network using software,information,and communication *** assisted living(AAL)allows the incorporation of emerging innovations into the routine life events of *** learning(ML)teaches machines to learn from human experiences and to use computer algorithms to“learn”information directly instead of relying on a *** the sample size accessible for learning increases,the performance of the algorithms *** paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network(CNN)algorithm for fast analysis,improved decision-making,and enhanced treatment *** simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

关键词： Smart healthcare system neural network machine learning

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A Survey on Trustworthy Edge Intelligence: From security and Reliability To Transparency and Sustainability

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1-1页

作者： Wang, Xiaojie Wang, Beibei Wu, Yu Ning, Zhaolong Guo, Song Yu, Fei Richard School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong China School of Information Technology Carleton University Ottawa ON Canada

Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we present enabling technologies for trustworthy EI systems. Subsequently, we summarize several important issues that hinder the achievement of trustworthy EI and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues. IEEE

关键词： Mobile edge computing

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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