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

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for 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

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Coarse registration of color point clouds by combining color feature and geometric feature 12

Coarse registration of color point clouds by combining color...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Sun, Peng Jiang, Shuhang Luan, Haiyang Wang, Chen Wang, Wanqi Liu, Yu School of Opto-Electronic Engineering Changchun University of Science and Technology Jilin Changchun130022 China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu Sichuan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Coarse registration is the initial step of aligning a point clouds with other clouds, aiming to put two point clouds in the correct position. There are many coarse registration methods, and among them, the SAC-IA (Sample Consensus Initial Alignment) is widely used. It selects corresponding point pairs by matching the geometric features of point clouds. It has high registration accuracy and fast registration speed. However, when the geometric features of the point clouds are relatively simple or not distinctive, its registration performance may not be very effective. With the development of color image sensors, acquiring color point clouds has become increasingly important. The RGB information of point clouds can effectively compensate for the shortcomings of the SAC-IA algorithm. The color characteristics of the interested points are extracted by fusing the color information of feature points and their neighboring points, including the first-order moment of point clouds color and the CFH of the point clouds, based on the traditional SAC-IA algorithm. Experiments have shown that the improved SAC-IA algorithm has better accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Color

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Intermittent Fault Diagnosis of Split-Star Networks and its Applications

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IEEE Transactions on Parallel and Distributed Systems 2023年第4期34卷 1253-1264页

作者： Song, Jiankang Lin, Limei Huang, Yanze Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou Fujian350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou Fujian350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability t I PMC(S n 2) tIPMC(Sn2) of n-dimensional split-star network Sn2 under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the n-dimensional split-star network Sn2 under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN. © 1990-2012 IEEE.

关键词： Fault detection

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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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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for 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

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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

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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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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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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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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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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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