检索结果-内蒙古大学图书馆

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

引用

Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

来源：评论

学校读者我要写书评

暂无评论

mpiPython: A Streamlined Python Collective Communication Library to MPI 24

mpiPython: A Streamlined Python Collective Communication Lib...

引用

9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Lee, Jaden Jinu Nava, Judah J. Lee, Hanku Computer Science & Information Systems Department Minnesota State University Moorhead MoorheadMN United States

ISBN: (纸本)9798400717161

Python's popularity as an interpreted language, particularly among scientists and engineers, is due to its ease of use and flexibility, despite certain performance limitations. Enhancing Python for parallel programming opens significant opportunities in both parallel and cloud computing environments. mpiPython, a Python library designed for message-passing, enables Single Program Multiple Data (SPMD) execution and supports efficient parallel processing. One of its key advantages is its simplicity - both in installation and use - making it accessible to a wide range of users without compromising performance. This paper introduces an extended version of mpiPython, which addresses shortcomings in collective operations for parallel computing while maintaining the library's strengths: ease of installation and minimal overhead. mpiPython is designed with a direct implementation in C, avoiding additional layers, unlike mpi4py, allowing for more efficient communication without unnecessary overhead. The latest version introduces key enhancements to collective communication, including MPI All-Gather, MPI-Send, and MPI-Receive functions. These additions significantly improve performance, particularly in large-scale distributed systems, while preserving mpiPython's focus on being lightweight and user-friendly. Our results show that mpiPython offers competitive performance compared to other MPI libraries while remaining accessible to users who prioritize simplicity and efficiency. The latest fully functional release of mpiPython is now officially available, providing a streamlined solution for Python-based parallel computing. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

引用

computers, Materials & Continua 2023年第1期74卷 897-914页

作者： Sandeep Kumar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdul Khader Jilani Saudagar Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Social media,like Twitter,is a data repository,and people exchange views on global issues like the COVID-19 *** media has been shown to influence the low acceptance of *** work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to *** work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 *** paper introduces a sigmoidal particle swarm optimization(SPSO)***,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing *** proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature *** Covid-19 vaccination tweets dataset was created and used for training,validating,and *** proposed approach outperformed considered algorithms in terms of ***,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection *** shows that augmentation improves the overall accuracy of tweet *** the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

关键词： Twitter data analysis sentiment analysis social media analytics swarm intelligence COVID-19 vaccine

来源：评论

学校读者我要写书评

暂无评论

Intelligent Anomaly Detection System Based on Ensemble and Deep Learning 26

Intelligent Anomaly Detection System Based on Ensemble and D...

引用

26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Baniya, Babu Kaji Rush, Thomas Bradley University Dept. of Computer Science and Information Systems PeoriaIL United States

ISBN: (纸本)9791188428120

The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ens

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Advancing Plant Disease Prediction Accuracy with ViT Based Deep Learning Model

Advancing Plant Disease Prediction Accuracy with ViT Based D...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent systems and Communications, COSMIC 2024

作者： Sam, Felicita Kavitha, B.R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu Vellore India

ISBN: (纸本)9798331517892

Agriculture is the key source for many people's livelihoods and an important contributor to a country's economy. There is a huge demand to streamline the process in the field of agriculture by integrating Computing into the traditional way of farming. Computing plays a key role in handling this sector with many newer revolutions that would automate the various processes involved in overall farming. Particularly, machine and deep learning based models have shown remarkable results in smart farming. Rice crop is considered in this work which is the major crop in India. It is necessary to meet the demands of the growing population in terms of sufficient production of this crop. One of the key issues in this crop is the plant diseases that impact the overall crop yield production considerably. Among the various diseases, the most commonly occurring disease is White stem Borer. Diagnosing the disease at an early stage could treat the problem at the earliest to ensure the appropriate amount of yield production. In this paper, a Vision transformer (ViT) based deep learning model is proposed to predict the white stem borer disease on the PlantDoc dataset and the results obtained are compared with the other promising CNN based pre trained models like VGG19, InceptionV3, Resnet50. The experimental output shows that Vision transformer based model outperforms the other proven models with an accuracy of 96%. © 2024 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

A learning artificial visual system for motion direction detection

引用

Neural Computing and Applications 2024年第27期36卷 17181-17197页

作者： Chen, Tianqi Kobayashi, Yuki Todo, Yuki Tang, Zheng Faculty of Electrical and Computer Engineering Kanazawa University Kakuma Ishikawa Kanazawa9201162 Japan Department of Intelligence Information Systems University of Toyama Toyama9308555 Japan

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

引用

Intelligent Automation & Soft Computing 2023年第9期37卷 2849-2863页

作者： Ahmed S.Almasoud Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the *** Detection System(IDS)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified *** this back-ground,the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT *** aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced *** attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform *** addition,Enhanced Cat Swarm Optimization based Feature Selection(ECSO-FS)approach is followed to choose the optimal feature ***,Mayfly Optimization(MFO)with Twin Support Vector Machine(TSVM),called the MFO-TSVM model,is utilized for the detection and classification of ***,the MFO model has been exploited to fine-tune the TSVM variables for enhanced *** performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several *** comparative study concluded that the EMML-CADC model is superior to other models under different measures.

关键词： Metaheuristics cyberattack detection machine learning cat swarm optimization security

来源：评论

学校读者我要写书评

暂无评论

POA-Net: Dance Poses and Activity Classification Using Convolutional Neural Networks

POA-Net: Dance Poses and Activity Classification Using Convo...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Agarwal, Aditya Jha, Parth Jain, Ojas Bera, Asish Bits Pilani Department of Computer Science and Information Systems Pilani campus Rajasthan 333031 India

ISBN: (纸本)9798350364866

Dance poses represent a complex human body-part movement, and express emotions and gesture. Dance pose classification is a challenging problem in computer vision. Convolutional Neural Networks (CNNs) have witnessed significant performance improvements in recognizing dance poses from images and videos. Most of the dance datasets in existing works are video-based and are not available publicly. This work contributes an image dataset representing 8 new dance styles blended with the Indian and international dance themes, called Dance-8. These unique 8 dance styles are combined with the Dance-12 public dataset for improving the posture diversity and dataset size. This extended dataset is called Dance-20. A custom CNN is developed for dance POses and Activity classification, named POA-Net. All three dance datasets have been evaluated using standard base CNNs and POA-Net. The POA-Net has attained an accuracy of 73.27% on Dance-8, 82.10% on Dance-12, and 73.10% on Dance-20. These performances are better than those of standard backbones, such as VGG16 and Inception-V3. The best accuracy of 81.57%, 85.08% and 76.73% has been achieved by MobileNet-v2 on these Dance-8, 12, and 20 datasets, respectively. Moreover, POA-Net has achieved the state-of-the-art accuracy of 99.74% on the DIAT, which is a radar-based human action image dataset. Dance-8 and Dance-20 datasets are available at: https://***/datasets/m6syhcn2nh/1. © 2024 IEEE.

关键词： Convolutional Neural Networks (CNNs) Dance posture recognition Human action classification

来源：评论

学校读者我要写书评

暂无评论

D-CIDE: An Interactive Code Learning Program 15

D-CIDE: An Interactive Code Learning Program

引用

15th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2024

作者： Grant, Lukas Tennyson, Matthew F. Owen, Jason Computer Science and Information Systems Murray State University MurrayKY42071 United States

ISBN: (纸本)9781950492787

This paper introduces D-CIDE (Distributed Classroom Integrated Development Environment), a tool that is designed to improve student-teacher interactions in programming classes. D-CIDE’s main objective is to provide more meaningful interactions between teachers and students. Its goal is to create a more seamless and interactive learning environment for everyone who uses it. D-CIDE is a distributed integrated development environment (IDE), where the teacher (host) can manage and interact with the IDEs of all students (clients). It makes use of server-client interactions to allow live sharing and editing of code, making it a useful tool for demonstrating coding techniques and quickly addressing student questions. The front-end was developed using HTML, CSS, and JavaScript, and provides a way for the students and teachers to interact with each other. The back-end is made with JavaScript and NodeJS and handles data processing and transmission. The effectiveness of D-CIDE was analyzed through a classroom case study involving a small group of students. The study measured students' engagement, enjoyment, and learning outcomes using D-CIDE compared to traditional teaching methods. Results indicated an increase in student engagement and satisfaction when D-CIDE was used, as well as an improvement in students' learning experiences. © IMCIC 2024. All rights reserved.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Enhancing Financial Sector Cybersecurity: A Multi-Component Framework for Supply Chain Risk Mitigation 6

Enhancing Financial Sector Cybersecurity: A Multi-Component ...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Kiran, Suryaa Praveen, Yomal Silva, Akila Bagya, Yasith Yapa, Kavinga Siriwardana, Deemantha Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Supply chains are being used at scale within new industries, mainly in the financial sector and the cyber security risks emerging in this area have been on the rise. Given recent attack scenarios the mitigation strategies taken within these areas are limited. This research paper focuses on the main areas where the risk can be reduced to provide an overall effective solution to reduce the risks. However, there are certain challenges in minimizing the risks associated, such as third-party vendor identification, classifying and securing data, and gathering relevant external threat information. This research utilizes a centralized risk assessment gateway to assess the security level of each third-party vendor. Further, there will be a secure transfer of text and image data once the vendors have been assessed and these will be done in correlation with a zero-trust policy engine where data classification occurs based on unique trust levels. In addition, it will also use machine learning to tailor the external cyber threat feeds to provide valuable insights regarding threat attacks, vulnerabilities, and patches within the sector to gain more granular intelligence and reduce analysis time. The overall solution will serve as a zero-trust approach to mitigate the attacks within the financial fields of the supply chain sector. © 2024 IEEE.

关键词： data classification machine learning risk assessment supply chains zero trust policy engine

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：