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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm Walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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Secure and Efficient Blockchain-Based Access Control Scheme with Attribute Update

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IEEE Transactions on Consumer Electronics 2024年

作者： Zuo, Yuting Xu, Li Li, Jiguo Li, Jie Wang, Xiaoding Piran, Md. Jalil Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Lab Shanghai200240 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

Secure data access control is widely concerned in cyber Physical Systems (CPSs) to protect consumer rights. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an ideal solution that supports fine-grained access control. It is a pity that they fail to consider the attribute control and the verifiable legitimacy when consumer's attributes are updated. Additionally, the existing revocable CP-ABE schemes are unsuitable for resource-constrained consumer electronic devices due to their high computational and storage overhead. This paper proposes a blockchain-based access control scheme with attribute update. A new revocation mechanism is designed to guarantee the forward/backward security of the attribute update. The lightweight ciphertext update protocol is proposed to optimize the computation cost. To realize secure and efficient attribute management, this paper designs the on-chain/off-chain separation storage mode, and proposes the traceability-based attribute set generation and validation algorithm. This paper performs the formal security proof. The experimental results demonstrate that the scheme is efficient in terms of encryption and decryption. © 1975-2011 IEEE.

关键词： Ciphertext

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Interference Mitigation in D2D Communication Underlying Cellular Networks:Towards Green Energy

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computers, Materials & Continua 2021年第7期68卷 45-58页

作者： Rana Zeeshan Ahamad Abdul Rehman Javed Shakir Mehmood Mohammad Zubair Khan Abdulfattah Noorwali Muhammad Rizwan Department of Computer Science Xian Jiaotong UniversityXi’anChina Department of Cyber Security Air UniversityIslamabadPakistan Department of Information and Technology Northwest UniversityXianChina Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Electrical Engineering Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Kinnaird College for WomenLahore54000Pakistan

Device to Device(D2D)communication is emerging as a new participant promising technology in 5G cellular networks to promote green energy networks.D2D communication can improve communication delays,spectral efficiency,system capacity,data off-loading,and many other fruitful scenarios where D2D can be ***,induction of D2D communication in reuse mode with the conventional cellular network can cause severe interference issues,which can significantly degrade network *** reap all the benefits of induction of D2D communication with conventional cellular communication,it is imperative to minimize interference’s detrimental *** power control can minimize the negative effects of interference and get benefits promised by D2D *** this work,we propose two power control schemes,Power Control Scheme 1(PCS 1)and Power Control Scheme 2(PCS 2),to minimize the interference and provide performance *** results observe improvements with PCS 1 and PCS 2 as compared to without using any power control scheme in terms of data rate in both uplink and downlink communication modes of Cellular User Equipment(CUE).

关键词： Interference mitigation green IoT green networks D2D power control spectral efficiency reuse mode

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Enhanced Word Embedding with CNN using Word Order Information for Sentiment Analysis 20

Enhanced Word Embedding with CNN using Word Order Informatio...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Atandoh, Peter Fengli, Zhang Hakeem, Paul Atandoh Acheampong, Edward Mensah Addo, Daniel Appiah-Twum, Michael Adu-Gyamfi, Daniel Blankson, Richard Safo Giza Wi, Tohye Tewodros School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Mercer University Department of Mathematics MaconGA31207 United States School of Resources and Environment University of Electronic Science and Technology of China Chengdu China C. K. Tedam University of Technology and Applied Sciences Department of Cyber Security and Computer Engineering Technology Navrongo UE/R Ghana Summit Economics LLC Colorado United States

ISBN: (纸本)9798350318982

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7%, 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE. © 2023 IEEE.

关键词： Sentiment analysis

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Power amplifier circuit defect detection based on improved Patch SVDD

Power amplifier circuit defect detection based on improved P...

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作者： Xie, Zhonghui Kong, Wa Zhou, Xinyu Zhang, Wence Zhong, Yujie Xia, Jing School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang China Department of Electronic Information Engineering Hong Kong Polytechnic University Hong Kong

In the manufacturing process of power amplifier (PA), various defects on the circuit surface will seriously affect the circuit performance and its operation. To solve the above problems, this article proposes a circuit defect detection method based on improved patch-based support vector data description algorithm (Patch SVDD), which uses contrastive learning to enhance the feature extraction ability of neural network. An image calibration algorithm is used for calibration preprocessing to enhance the robustness of the model. The Euclidean distance between a query image patch and its nearest normal patch is defined to be an anomaly score. For verification, three defects in the PA circuit, including components missing, solder contamination and component orientation dislocation, were detected. The experimental results show that, compared with conventional method, the area under receiver operating characteristic (AUROC) of the improved anomaly detection model increased from 90.1% to 95.1%, which improves the detection accuracy effectively. © 2023 John Wiley & Sons Ltd.

关键词： Anomaly detection

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Diffusion-based dynamic super-dense candidate boxes with random center points for 3D object detection

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Applied Soft Computing 2025年 178卷

作者： He, Si-Heng Wang, Zi-Jia Wang, Yuan-Gen Zhou, Yicong Kwong, Sam Computer Science and Cyber Engineering Guangzhou University Guangzhou China Department of Computer and Information Science Faculty of Science and Technology University of Macau China Department of Computer Science Lingnan University Hong Kong

Diffusion models have achieved promising results in image generation, but their applications in 3D object detection still need further exploration. In this paper, we design a novel model DiffCandiDet based on dense heads with Gaussian distributed center points for 3D object detection, which effectively integrates the anchor-based method and the Gaussian random noise-based method to leverage the powerful denoising and reconstruction capabilities of the diffusion model. To achieve the learning balance for multi-class 3D object detection, we propose a Dynamic Super-dense Candidate Boxes (DSCB) strategy. Notably, DiffCandiDet addresses the issue of traditional models struggling to detect pedestrians walking side by side. In addition to Gaussian distribution, we also propose a DSCB strategy based on discrete uniform distribution (DUCandiDet) and continuous uniform distribution (CUCandiDet), to reduce the runtime consumption and enhance the robustness of the model. Extensive experiments show that DiffCandiDet achieves competitive results on both KITTI and Waymo Open Datasets. DiffCandiDet ranks 1st on the KITTI validation set in the Car and Pedestrian detection leaderboard. Code is available at https://***/SiHengHeHSH/DiffCandiDet. © 2025 Elsevier B.V.

关键词： Object detection

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Diagnosis of Plant Leaf Disease using Vision Transformer

Diagnosis of Plant Leaf Disease using Vision Transformer

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International Conference on Communications and Signal Processing

作者： Ashik Sundaraj Deva Priya Isravel Julia Punitha Malar Dhas Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In precision agriculture, it is crucial to have a reliable system for identifying diseases and suggesting measures to maintain crop health and enhance yield performance. Addressing the persistent challenge of accurate and timely identification of plant illnesses, this study introduces a new understanding of the approach utilizing the Vision Transformer (ViT) architecture. Leveraging a diverse dataset encompassing both diseased and healthy plant images, the ViT model demonstrates efficacy by achieving an precision of 94% in recognizing and categorizing diseases based on plant leaves. The success of this transformer-based methodology underscores its potential to significantly enhance the field of agricultural diagnostics, offering a promising solution to improve crop management practices and mitigate potential losses in yield.

关键词： Training Plant diseases Plants (biology) Crops Signal processing algorithms Signal processing Transformers

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Diabetic Condition Classification using Supervised and Unsupervised Machine Learning Techniques: A Use Case for HealthTech Industry 4

Diabetic Condition Classification using Supervised and Unsup...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Bongale, Anupkumar M. Biradar, Santoshkumar Pise, Priya Suke, Amita Priyadarshan Uke, Nilesh Dharrao, Deepak S. Symbiosis Institute Of Technology Department Of Aiml Pune Campus Pune India G H Raisoni College Of Engineering And Management Department Of Cyber Security And Data Science Pune Wagholi India Indira College Of Engineering And Management Department Of Ai And Ds Pune India Trinity Academy Of Engineering Pune India Symbiosis Institute Of Technology Department Of Cse Pune Campus Pune India

ISBN: (纸本)9798350384628

Diabetes is a very underestimated and, in many cases, an ignored disease. People do not understand the seriousness of diabetes. Many people think diabetes is just something because of which one cannot consume sweets. This, however, is not true. Diabetes is a very serious problem where symptoms can range from being very thirsty all the time to even death. There are many cases where diabetic people had to get their leg, or some other limb amputated because of it. Thus, people have to be made cognizant of diabetes' seriousness. This article dives deep into this disease and lists the different types of diabetes, and the way people can know if they are diabetic. Also, there is an in-depth literature review of research papers ranging from 2002-2020. Furthermore, a deep dive in the project of diabetes prediction is present. The techniques used to classify into 'diabetic' or 'non-diabetic' are Random Forest, Logistic Regression and K Nearest Neighbor algorithms which all belong to the supervised machine learning algorithms. Logistic Regression uses sigmoid function to classify into different classes, Random Forest approach uses multiple Decision Trees and the majority decision of them all for the final output. Moreover, K Nearest Neighbor technique uses the concept of Euclidean Distance to decide which class is suitable for any new coming datapoint. © 2023 IEEE.

关键词： Decision trees

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A Deepfake Image Classifier System for real and Doctored Image Differentiation

A Deepfake Image Classifier System for real and Doctored Ima...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Juanith Mathew Thomas V. Ebenezer Rohan Paul Richard Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet" architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: Dataset I comprises authentic images while Dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we’ll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%.

关键词： Deep learning Training computers Deepfakes Computational modeling Neural networks Medical services

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