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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism

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science China(Information sciences) 2023年第3期66卷 273-286页

作者： Weixuan TANG Bin LI Weixiang LI Yuangen WANG Jiwu HUANG Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science and Cyber Engineering Guangzhou University

Image steganography is the art and science of secure communication by concealing information within digital images. In recent years, the techniques of steganographic cost learning have developed rapidly. Although the existing methods can learn satisfactory additive costs, the interplay of different pixels' embedding impacts has not been considered, so the potential of learning may not be fully exploited. To overcome this limitation, in this paper, a reinforcement learning paradigm called Jo Po L(joint policy learning) is proposed to extend the idea of additive cost learning to a non-additive situation. Jo Po L aims to capture the interactions within pixel blocks by defining embedding policies and evaluating contributions of embedding impacts on a block level rather than a pixel level. Then, a policy network is utilized to learn optimal joint embedding policies for pixel blocks through interactions with the environment. Afterwards,these policies can be converted into joint embedding costs for practical message embedding. The structure of the policy network is designed with an effective attention mechanism and incorporated with the domain knowledge derived from traditional non-additive steganographic methods. The environment is responsible for assigning rewards according to the impacts of the sampled joint embedding actions, which are evaluated by the gradient information of a neural network-based steganalyzer. Experimental results show that the proposed non-additive method Jo Po L significantly outperforms the existing additive methods against both feature-based and CNN-based steganalzyers over different payloads.

关键词： information hiding non-additive steganography steganalysis cost learning image processing

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Oral Cancer Using Deep Learning and Auto-Fluorescence Image Analysis

Oral Cancer Using Deep Learning and Auto-Fluorescence Image ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Muhammed Yaseer, P. Arul Xavier, V.M. Shyni, S.S. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Tamilnadu Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Tamilnadu Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Poor treatment outcomes result from the fact that oral cancer is frequently identified at an advanced stage, making it a serious and sometimes lethal disease. Thus, it is essential to develop quick and accurate procedures for detecting oral cancer in its early stages. In this work, we present a unique non-invasive detection technique for evaluating auto-fluorescence signals in oral tissues utilizing convolutional neural networks ResNet50 architecture. Biological tissues naturally produce auto-fluorescence signals, and variations in these signals may be a marker of the existence of malignant cells. Our approach seeks to precisely identify malignant and normal oral tissues by capturing these minute variations in auto-fluorescence patterns. Using a sizable dataset of pictures of oral tissue, we trained and evaluated our RestNet50 model, and the results showed impressive accuracy rates of 89%. The better performance of our detection method can be attributed to the effective capture of high-level features made possible by the ResNet50 architecture. Furthermore, the suggested approach demonstrated quick processing speeds, which allowed it to be used for real-time clinical applications. This approach may greatly enhance patient prognosis and treatment results by identifying oral cancer in its early stages with high accuracy of 89% when compared to VGG16 and VGG19. Although more validation research is necessary before the suggested approach can be used in clinical settings, it shows promise in facilitating early diagnosis and intervention to enhance patient outcomes. © 2024 IEEE.

关键词： Fluorescence

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Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions 2

Vision-based UAV Detection in Complex Backgrounds and Rainy ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Pranita Gupta, Keshav Jain, Amit Kumar Vishakha Jain, Abhishek Jain, Arpit Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Grd Institute of Management and Technology Department of Computer Science and Engineering Dehradun India Coer University Department of Cyber Security Roorkee India Uttaranchal University Department of Computer Science and Engineering Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350371055

Studies aimed at detecting UAVs in real time using processer vision and deep learning are in their infancy. Although there are many possible advantages to using unmanned aerial vehicles (UAVs), some people are concerned about the risks and misuse of these machines. All potential threats to safety, privacy, and security fall under this category. It is common practice to combine hardware components, such as cameras, with software components when developing vision-based detection systems. Under difficult circumstances such as changing experiences, varying UAV sizes, complicated related settings, and light to heavy rain, this research evaluates the performance of popular and cutting-edge vision-based object-identification algorithms in detecting unmanned aerial vehicles (UAVs). Two datasets were created;one utilized the sky and the other had a more complex background. The study's overarching goal was to evaluate how various methods fared in this particular context. In YOLOv5, YOLOv8, Retina Net, and Faster-RCNN, we examine and evaluate indicators with one stage and two stages. The results of this research could provide light on the performance of the chosen models in difficult settings and lead to the creation of more trustworthy UAV detection systems. © 2024 IEEE.

关键词： Cameras UAV sizes Unmanned aerial vehicles Vision-based YOLOv5 and Faster-RCNN

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Deep Learning based Intelligent System for Skin Disease Detection 10

Deep Learning based Intelligent System for Skin Disease Dete...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Dhanush, K.B. Arul Xavier, V.M. Shyni, S.S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamilnadu Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Tamilnadu Coimbatore India

ISBN: (纸本)9798350353068

Malignant and benign skin diseases significantly impact public health and personal well-being. Traditional machine learning models encounter difficulties in processing image datasets, which is crucial for early and accurate diagnosis. This work employed two pre-trained deep learning models, ResNet50 and VGG19, known for their precise and swift image processing capabilities. These models underwent training utilizing an extensive dataset of images from patients battling benign keratosis, Melanoma, and Melanocytic nevus (Mole). The models were then tasked to distinguish between these three conditions. The results were remarkable, with the models achieving accuracies of 91.3 % and 95.6 %, respectively. A comparative analysis revealed that the VGG19 model outperformed in accurately identifying these three diseases, surpassing traditional AI and machine learning methods. The trained VGG19 model was subsequently incorporated into an Android application to enhance user experience. © 2024 IEEE.

关键词： Diagnosis

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer science 2024年第6期5卷 1-9页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Ensuring Privacy and Integrity in IoT Supply Chains through Blockchain and Homomorphic Encryption

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IEEE Internet of Things Journal 2025年第13期12卷 25547-25558页

作者： Din, Ikram Ud Almogren, Ahmad Han, Zhu Guizani, Mohsen The University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Department of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle. © 2014 IEEE.

关键词： Data integrity

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A Survey of Machine Learning Applications in Medical Imaging for Neurodegenerative Disease Diagnosis 2

A Survey of Machine Learning Applications in Medical Imaging...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Nandhashree, K.R. Senthil Kumar, M. Susee, S.K. Department of Cyber Security SRM Valliammai Engineering College India Department of Computer Science and Engineering SRM Valliammai Engineering College India Department of Electronics and Communication Mohamed Sathak A J College of Engineering India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

The increasing prevalence of brain degenerative disorders particularly Alzheimer's and Parkinson's, have incurred a dire need for effective diagnostic methodologies. With advancements in medical imaging techniques and Artificial Intelligence (AI), transformative potential exists to enhance early detection and disease classification. This review examines the recent progress made in the use of diagnostic medical imaging and artificial intelligence (AI) techniques for the early detection of Alzheimer's disease and Parkinson's disease. Despite significant progress, a comprehensive overview is lacking. The review aims to summarize and synthesize existing methods, critically evaluate their strengths and limitations, and identify emerging trends. Multimodal imaging data, combining information from different sources like MRI, PET, and SPECT, has shown progress in improving diagnostic accuracy and providing insights into disease progression. Advanced AI techniques, such as transfer learning and generative adversarial networks, have further enhanced the performance of diagnostic models. Previous studies have predominantly focused on using standalone techniques, often leaving the integration of multi-modal imaging and deep learning methodologies that could lead to more robust diagnostic frameworks. This literature review addresses this gap by synthesizing current research on medical imaging modalities, AI applications, and their implications in diagnosing Alzheimer's and Parkinson's diseases. This review seeks to connect technological innovations with clinical applications to establish more robust and dependable diagnostic methods. © The Institution of engineering & Technology 2024.

关键词： Generative adversarial networks

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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cyber Threat Intelligence (CTI): An Analysis on the Use of Artificial Intelligence and Machine Learning to Identify cyber Hazards

Cyber Threat Intelligence (CTI): An Analysis on the Use of A...

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International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Kant, Neelima Amrita Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida201306 India Department of Computer Science and Engineering School of Engineering and Technology Center for Cyber Security and Cryptology Sharda University Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9789819998104

Data transport volume and scope on networks are growing daily due to the quick advancements in network technology. It is challenging for cybersecurity specialists to keep track of every action taking place on the network because of the constantly growing density of networks. This circumstance has led to an increase in the complexity and intensity of threats and attacks. It is harder to detect and identify irregularities in network activities because of frequent and sophisticated cyberattacks. A well-crafted cybersecurity strategy now includes cyber threat intelligence (CTI), which is a crucial foundation. Automating the detection of cyberattacks as well as speedy attack type analysis and predication are all made possible by machine learning (ML), which offers a number of tools and techniques. The strategies for using machine learning (ML) to identify assaults are discussed in this article. Threat intelligence can help security teams defend against a constantly evolving threat environment before, during, and after an attack if used properly. By analyzing attackers and comprehending their tactics and goals, groups may create cyber defenses that are more effective, delicate, and resilient. However, due to two significant flaws, its usefulness is still in question. First, current methods are unable to detect unknown Indicator of Compromise (IoC), and second, they are unable to automatically produce categorized CTIs which renders CTI sharing. As a result, the objective of this paper is to present a complete analysis of cyber threat identification using intelligent techniques. Additionally, we covered the issues and solutions related to machine learning applications utilized in network assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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