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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Analysis of Synthetic Data Generation Techniques in Diabetes Prediction 2nd

Analysis of Synthetic Data Generation Techniques in Diabet...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Das, Sujit Kumar Roy, Pinki Kumar Mishra, Arnab Department of Computer Science and Engineering National Institute of Technology Silchar Assam788010 India

ISBN: (纸本)9789819934805

The problem of inadequate and class imbalanced data is one of the major problems in the classification tasks. Therefore applying synthetic data generation (SDG) approaches to handle class imbalances can be useful in improving Machine Learning (ML) classifier’s performance. The aim of this work is to explore various SDG approaches to improve diabetes prediction using Pima Indian Diabetes Dataset (PIDD). We have also proposed a hybrid approach of SDG by combining the idea of popularly used SDG techniques Synthetic Minority Oversampling TEchnique (SMOTE) and SVM-SMOTE (Support Vector Machine-Synthetic Minority Oversampling TEchnique), named as SSVMSMOTE. The idea is to divide training data into equal halves and apply SMOTE and SVM-SMOTE separately to sub-training samples. The approach has successfully overcome the limitation of SMOTE and SVM-SMOTE. A set of classifiers namely Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Gaussian Naive Bayes (GNB), AdaBoost (AB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), and Light Gradient Boosting (LGM) are trained on the combined resampled training data and tested on hold out testset. The experiment shows that boosting classifiers, XGB, and GB outperformed other considered classifiers. Further, the XGB classifier, with the help of the proposed SDG technique, achieved the highest average accuracy of 0.9415. The proposed approach also achieved promising results in terms of other important evaluation metrics such as F-Scores, AUC, Sensitivity, and specificity. Therefore, such an impressive result of the proposed approach suggests its applicability in the real-life decision-making process. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Quantum inspired genetic algorithm and optimization of queuing delay

Quantum inspired genetic algorithm and optimization of queui...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Roy, Arnab Pachuau, Joseph L. Singh, Nongmeikapam Brajabidhu Saha, Anish Kumar National Institute of Technology Department of Computer Science and Engineering Assam Silchar788010 India

ISBN: (纸本)9798350350821

Queuing delay is the waiting time for the packets in the queue before processing. It significantly impacts the network compared to processing, and transmission delays. The delay occurs when a packet suddenly arrives faster than the processing of packets at different time. Minimum queuing delay is required to have better network control and high efficiency. In this paper, we proposed the quantum inspired genetic algorithm to obtain the minimum queuing delay and maximum arrival rate in a network. A quantum inspired crossover and mutation technique is designed. It provides an optimal point between maximum average arrival rate with minimum average queuing delay. The performance and convergence of the proposed algorithm are evaluated. The inspired algorithm can help to solve other optimization problems as well. © 2024 IEEE.

关键词： Genetic algorithms

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Detecting ugly and derogatory words in Serbian language using a web browser extension 32

Detecting ugly and derogatory words in Serbian language usin...

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32nd Telecommunications Forum, TELFOR 2024

作者： Milakovic, Adrian Jocovic, Vladimir Cincovic, Jelica Micovic, Marko Radenkovic, Uros Draskovic, Drazen University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Serbia

ISBN: (纸本)9798350391053

In natural language processing, data acquisition and preprocessing techniques are significant for experiments involving training models on cleaned data. This paper describes the formation of a dataset of ugly and derogatory words in the Serbian language and the development of a web extension to analyze texts in which such words appear and then their censoring. Developing such an add-on is essential in creating a safer and healthier digital environment for young users who are just developing their Internet habits and adults who want to avoid unwanted content. It is crucial to point out that there are few software tools for natural languages with limited resources, such as the Serbian language. This software could analyze the frequency of certain foul words on different sites, which would contribute to a better understanding of the impact of such language on users. © 2024 IEEE.

关键词： Web browsers

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Exploring the Impact of Node Velocity on Communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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Sarcasm Detection in English-Hindi Code-Mixed Text Data 2

Sarcasm Detection in English-Hindi Code-Mixed Text Data

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Prajapati, Abhinav Singh, Ajeet Arora, Vasudha Bansal, Nidhi Department of Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India Department of Computer Science & Engineering Moradabad Institute of Technology Uttar Pradesh Moradabad India Nagpur Campus Pune India

ISBN: (纸本)9798331541217

Sarcasm-also termed as verbal irony, when the speaker presents something in a mocking or humorous way such that he or she actually conveys the opposite of what they seem to say. This article has a detailed analysis of sarcasm detection in English-Hindi code-mixed texts based on the difficulties one faces while identifying sarcasm in linguistically complex data. Several machine learning and deep learning methods are adopted and compared, Long Short-Term Memory, Bidirectional Encoder Representations from Transformers, and Support Vector Machines are some of them. In addition, the complexity in the detection of sarcasm is further compounded by the nuance of code-mixed language because the former does not have any tradition that can be adapted to traditional text classification methods. Feature extraction and the subsequent decision-making processes are also factored with broader implications for sentiment analysis. The results of the models analysed underwent a comparative analysis, which showed that the model produced out of the combination of BERT and Gated Recurrent Unit (GRU) surpasses other models to be used for this particular task with its detection accuracy rate, said to be the best in determining sarcasm. © 2024 IEEE.

关键词： Support vector machines

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Medical Plants Identification Using Leaves Based on Convolutional Neural Networks 4th

Medical Plants Identification Using Leaves Based on Convolut...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Ch S N L S Sai Baba, B. Swathi, Mudhindi Kiran, Kompella Bhargava Bharathi, B.R. Matta, Venkata Durgarao Veenadhari, Ch. Lakshmi Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488870

The ayurvedic medicines have played a crucial role in health system, only a few experts could identify the herbs and know the ayurvedic properties of these herbs. These medicines prepared from the herbs having less side effects as compared to other general medicines. Most of the patients and general medicine users with different diseases are not unaware of the existence of herbal plants and their medical uses and benefits. To make ease of identifying the plants and its medical properties based on the leaf structure, authors developed a system having three architectures which works with Convolutional Neural Networks. Resnet-18, Resnet-50, MobileNet-V2 architectures were used in freeze and unfreeze layers settings. Authors considered ten different kinds of herbal leaves for implementation of the system, in which two thirds of the data used for training and one third for testing. The overall performance of this architecture is checked using accuracy measure and it is observed that three models with freeze layers were showing good performance. Out of these three architectures, Resnet-50 shown accuracy of 95.33%. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Convolution

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BIOWATCH: Detecting the Lake Pollution to Safeguard Biotic Life

BIOWATCH: Detecting the Lake Pollution to Safeguard Biotic L...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Shalini, S. Mounika Sree, K. Prajwal, M.H. Nitin Reddy, N.V. Govardhan Reddy, P. Dayananda Sagar Academy of Technology and Management Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

This study presents an integrated system devised to combat plastic pollution in lakes autonomously, eliminating the need for human intervention. Utilizing sensor data and camera imagery processed through the YOLO algorithm. The detected plastic is then used to compute its density, which is subsequently compared against a predetermined threshold. Upon surpassing the threshold, an automated email notification containing the density values is dispatched to relevant authorities. Furthermore, water quality sensors are integrated to monitor environmental conditions continuously. Regular updates are provided to facilitate proactive measures in pollution prevention. This initiative underscores the application of cutting-edge technology to address environmental issues and uphold the well-being of aquatic ecosystems. Through automated detection and monitoring processes, the system offers a sustainable approach to combat plastic pollution in lakes, promoting environmental conservation efforts. © 2024 IEEE.

关键词： Lakes

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A Hybrid Approach for Agriculture Improvement Using Deep Learning 5

A Hybrid Approach for Agriculture Improvement Using Deep Lea...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Lakshmi, Pothineni Sai Babu, K.Suresh Basha, P. Hussain Pace Institute of Technology Sciences Department of Computer Science & Engineering AP Ongole India

ISBN: (纸本)9798350379945

Crop diseases have become more dangerous for farmers, reducing agriculture growth globally. Improving crop disease detection based on affected regions is very important. The proposed approach combines image processing and deep learning (DL) algorithms to develop automated systems. Including domain-specific knowledge, such as crop physiology and agronomy, can further improve the performance of these models. In this paper, a Hybrid Learning Approach (HLA) is developed to find the crop diseases and combines the data on crop-specific fertilizers with DL methods. VGG19, the pre-trained model, accurately finds patterns in crop images. Long Short-Term Memory (LSTM) is the model that detects and finds abnormal crop images based on the diseased patterns. The next step depends on identifying the disease and recommending the best fertilizer treatments by improving domain knowledge on types of crop diseases and nutrition-the crop dataset collected from actual crop disease images available in Kaggle. Finally, the results show that the proposed algorithm achieves high accuracy based on the obtained performance on disease detection rate. © 2024 IEEE.

关键词： Crop Diseases Deep Learning (DL) Hybrid Learning Long Short-Term Memory (LSTM)

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Aspect Based Sentiment Analysis in Bengali Text using Machine Learning and Deep Learning Algorithms 26

Aspect Based Sentiment Analysis in Bengali Text using Machin...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Lamia, Suhala Hasan, Md Mehedi Kamal, Abu Raihan Mostofa Hossain, Md. Azam Department of Computer Science and Engineering Gazipur Bangladesh International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Sentiment analysis (SA) has been used to monitor social media, help customers, perform market research, and gauge client sentiment to improve businesses' products and services over time. The conventional approach of SA often focuses on classifying sentiments into three main polarities: positive, negative, and neutral. Aspect based sentiment analysis (ABSA) is an advantageous methodology that facilitates a comprehensive analysis of consumer feedback. This approach not only determines the sentiment polarity of the text but also identifies the specific attributes of the product or service. While substantial research on ABSA has been conducted in English and other widely spoken languages, Bengali-language research on this topic is notably limited. This study explores machine learning and deep learning methodologies for analyzing Bengali text, particularly emphasizing on five different aspects and their corresponding emotions. This research employs several machine learning approaches, such as Decision Tree, Random Forest, XGBoost, Gradient Boosting, and Naive Bayes, to investigate both aspect and sentiment. The Gradient Boost model exhibits the best level of accuracy, reaching 67%, in the task of sentiment detection. On the other hand, the Random Forest model achieves an accuracy of 87.4%, in the task of aspect determination. In addition, several deep learning algorithms, including BiLSTM, BiGRU, and BERT, have been used in this study. Compared to the BiLSTM, and BiGRU models, BERT has the highest sentiment analysis and aspect accuracy, with 73.09% and 88.84%, respectively. © 2023 IEEE.

关键词： Aspect Based Sentiment Analysis Bengali Deep Learning Machine Learning Sentiment Analysis

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