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检索条件"任意字段=International Conference on WWW/Internet 2017 and Applied Computing 2017"

共 176 条记录，以下是1-10 订阅

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Proceedings of the international conference on www/internet 2017 and applied computing 2017

Proceedings of the International Conference on WWW/Internet ...

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international conference on www/internet 2017 and applied computing 2017

ISBN: (纸本)9789898533692

The proceedings contain 45 papers. The topics discussed include: role-based access control for cloud computing realized within HAPI FHIR;an early warning semantic-based model for emergency departments;integration between electronic health records standards using ontologies and rules;a study to classify hospital acquired complications based on electronic health record: the preprocessing phase;revisiting sesame architecture for 5G networks;reverse mediated information system: web-based retrieval of distributed, anonymous information;automatic improvement of terms used in focused crawling processes on web pages;an ontology-driven framework for data integration and dynamic service composition: case study in the oil & gas industry;a question and answer system for factual queries in Portuguese on DBpedia;a survey on ICT usage in education in Angola high polytechnical school;sociability impact on learner�s personality in classroom and e- learning environments: a comparative analysis;removing barriers in eGovernment: back office assessment;factors favoring the development of educational apps. usability study of mobile applications to teach programming;trajectories comparing based on matching and distance evaluation within Stiefel and Grassmann manifolds;benchmarking of bare metal virtualization platforms on commodity hardware;and blind quality algorithm to analyze streaming video contents in 5G networks.

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Web Technologies track at the 32rd ACM/SIGAPP Symposium On applied computing, SAC 2017, Web Technologies track at the 33nd ACM/SIGAPP Symposium On applied computing, SAC 2018 and oftware Development for Mobile Devices, Wearables, and the internet-of-Things Minitrack at the 51st Hawaii international conference on System Sciences, HICSS 2018

Web Technologies track at the 32rd ACM/SIGAPP Symposium On A...

引用

Web Technologies track at the 32rd ACM/SIGAPP Symposium On applied computing, SAC 2017, Web Technologies track at the 33nd ACM/SIGAPP Symposium On applied computing, SAC 2018 and oftware Development for Mobile Devices, Wearables, and the internet-of-Things Minitrack at the 51st Hawaii international conference on System Sciences, HICSS 2018

ISBN: (纸本)9783030284299

The proceedings contain 6 papers. The special focus in this conference is on applied computing. The topics include: Towards Pluri-Platform Development: Evaluating a Graphical Model-Driven Approach to App Development Across Device Classes;what Matters for Chatbots? Analyzing Quality Measures for Facebook Messenger’s 100 Most Popular Chatbots;linguistic Abstractions for Interoperability of IoT Platforms;Energy-Efficient Scheduling of Tasks with Conditional Precedence Constraints on MPSoCs.

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Edge computing Ransomware Detection in IoT using Machine Learning

Edge Computing Ransomware Detection in IoT using Machine Lea...

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international conference on computing, Networking and Communications (ICNC)

作者： Radhakrishna, Tejesh Majd, Nahid Ebrahimi Calif State Univ San Marcos Dept Comp Sci & Informat Syst San Marcos CA 92096 USA

ISBN: (纸本)9798350371000;9798350370997

The resurgence of ransomware has emerged as a pressing security threat in computer networks and internet connected machines and IoT devices. To address this challenge, accurate ransomware detectors are required to automatically detect and block the malicious traffic. Most ransomware detectors only detect whether the traffic is benign or ransomware. However, detecting the family of ransomware would be greatly useful to promptly eliminate or mitigate its destructive effects. To tackle this issue, we propose machine learning models that accurately detect each ransomware family. Our models aim to detect the ransomware network traffic and thwart it at the network edge before it enters the network. Considering that ransomwares directly work with the memory dump and file system, the information extracted from the operating system's functions on the memory dump is very useful to detect a ransomware attack. However, that information could be collected only when the ransomware has already infected the device and is actively disrupting the file system. In our research, we propose a framework that blocks the ransomware at the network edge. This restricts our research to using a dataset that extracts network traffic features with no access to the device's operating system's functionalities. An edge computing intrusion detection system is also beneficial for resource contained network devices, such as IoT, which have limited computational resources and cannot dynamically analyze the network traffic and run a strong intrusion detection system. We worked on CICAndMal2017 dataset and proposed a feature selection-based framework along with different machine learning models. We also applied a data augmentation technique to the training set to strengthen the data used to build our models. We extensively studied our proposed framework. Our experimental results demonstrated that chi-square feature selection with Random Forest and XGBoost models surpass other models and the state of the a

关键词： Ransomware Detection Network security Feature Selection Machine Learning

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Generation of Adversarial Malware Based on Genetic Algorithm and Instruction Replacement 23

Generation of Adversarial Malware Based on Genetic Algorithm...

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4th international conference on computing, Networks and internet of Things, CNIOT 2023

作者： Pan, Siyi Sun, Aoxiang Xu, Yingmei Liang, Zhuoqian Sun, Yuxia College of Information Science and Technology Jinan University Guangzhou China College of Information and Computational Science Jilin University China Guangdong Science and Technology Infrastructure Center China Guangdong Provincial Key Laboratory of High-Performance Computing China

ISBN: (纸本)9798400700705

With the development of cyberspace security attack and defense, the malware detection model based on machine learning is also facing the threat of adversarial examples. An important way to defend against such threats is to generate effective adversarial examples and then use them to conduct adversarial training on the model. For the end-To-end PE malware detection model, most of the existing generation technologies for adversarial examples adopt the method of inserting dead codes, but the dead codes are easy to be filtered out by preprocessing. A novel adversarial-example generation approach for PE malware, called AGA, is proposed based on a genetic algorithm and equivalent-instruction replacement. The experimental studies show that the AGA approach outperforms the existing generation approach, which is based on a particle-swarm-optimization algorithm, in terms of attack effectiveness and attack-generation efficiency. © 2023 ACM.

关键词： Genetic algorithms

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Semi-FLChain: Toward Secure and Efficient Semi-Supervised Federated Learning for IoT Using Blockchain 5

Semi-FLChain: Toward Secure and Efficient Semi-Supervised Fe...

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5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Qi, Yuanhang Liu, Guisong School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China School of Computer Science University of Electronic Science and Technology of China Zhongshan Institute Guangdong Zhongshan528402 China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Chengdu611130 China

ISBN: (纸本)9798350333442

This paper proposes a novel semi-supervised federated learning (SSFL) algorithm for internet of Things (IoT) systems using blockchain technology, called Semi-FLChain. The algorithm is designed to improve the efficiency and security of SSFL by combining hybrid data augmentation and Bayesian estimation to enhance the consistency regularization and pseudo-labeling methods in SSFL. The proposed algorithm aims to overcome the limitations of existing SSFL methods, such as the low accuracy of pseudo-labeling and the high computational cost of consistency regularization. The use of blockchain technology in Semi-FLChain further ensures the security and privacy of IoT devices and their data during the training process. The experimental results demonstrate that Semi-FLChain outperforms state-of-the-art SSFL algorithms in terms of both accuracy and efficiency, making it a promising solution for secure and efficient federated learning in IoT systems. © 2023 IEEE.

关键词： Blockchain

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Ontology-Based Cybersecurity for Well-Being, Aging and Health: A Scoping Review 3

Ontology-Based Cybersecurity for Well-Being, Aging and Healt...

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3rd IEEE international Mediterranean conference on Communications and Networking, MeditCom 2023

作者： Belani, Hrvoje Solic, Petar Perkovic, Toni Zovko, Kristina Ministry of Health Directorate for E-Health Zagreb Croatia University of Split Fesb Electronics and Computing Split Croatia

ISBN: (纸本)9798350333732

Well-being, aging and health cover important aspects of life of persons, communities and societies. As a study field, it aims to address all these aspects simultaneously and not excluding any aspect from it. Such environments have to be supported by various technology-enabled solutions, where cybersecurity is always critical. In order to organize efficient cybersecurity knowledge management, semantic constructs, such as ontologies, have to be introduced to the overall system. This work provides an overview of related work on ontology-based cybersecurity. The main contribution is the scoping review of cybersecurity ontologies applied to health, well-being and similar aspects of care. The review indicated the dual need for ontologies in such systems - the first to structure the meaning of well-being and health data, and the second to structure the meaning of cybersecurity approaches, measures, techniques, etc. to protect such data. Some health data challenges are also discussed and guidelines given for proper development of cybersecurity ontology for well-being, aging and health. © 2023 IEEE.

关键词： Ontology

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DDoSNet: A Deep Learning Model for detecting Network Attacks in Cloud computing 4

DDoSNet: A Deep Learning Model for detecting Network Attacks...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Srilatha, Doddi Thillaiarasu, N. School of Computing and Information Technology Reva University Bengaluru560064 India Sreenidhi Institute of Science and Technology Department of Cse Hyderabad501301 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Cloud services are the major target for malicious attacks as more organizations and individuals shift to the cloud. When DDoS assaults pose one of the most significant dangers to internet users and cloud services, this is especially important. As a result of DDoS assaults, cloud services cannot transmit optimum network infrastructure and sensitive information. Due to the flexibility of cloud computing, DDoS mitigation techniques in the cloud are significantly different from those used in conventional networks. When it comes to DDoS assaults, this article examines their influence on cloud resources and proposes a realistic intrusion detection system (IDS) referred to as DDoSNet. To evaluate models, this research study utilizes the CICIDS 2017 dataset. The intelligence approach of particle swarm optimization (PSO) is applied for feature selection. The optimal features are given input to the deep neural network (DNN) model to classify the given data as malicious or benign. The accuracy rate, recall, precision, and F1-score metrics are considered for model evaluation. The experimental results show an accuracy of 099.81%, precision of 099.77%, recall of 099.89% & F1 score of 099.83%. The presented system outperforms the conventional machine learning models. Hence, this model imparts huge confidence in providing security to cloud services. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Prediction of Heart Disease using LDL in Edge computing Systems 26th

Prediction of Heart Disease using LDL in Edge Computing Syst...

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26th annual international conference on Advanced computing and Communications, ADCOM 2020

作者： Kumari, K. Anitha Ananyaa, M. Keshini, P.S. Indusha, M. Department of IT PSG College of Technology Coimbatore India

ISBN: (纸本)9789811900181

Elevated levels of Low-Density Lipoprotein (LDL), referred to as the "Bad cholesterol", result in the risk of heart disease. LDL causes the buildup of fatty deposits at the walls of arteries and blood vessels, increasing the rate of risk of heart failure or stroke. A device named the glucometer is used to derive the parameter Triglyceride (TGL). TGL composition is used to calculate the Very Low-Density Lipoprotein (VLDL) level, followed by computation of the LDL level using additional parameters such as the Total Cholesterol and High-Density Lipoprotein (HDL). If the LDL level is less than 100 mg/dL then it is optimal. If the LDL level is higher than 100 mh/dL, then the patient is under the risk of heart disease/stroke. internet of Things (IoT) and Edge computing are two developing fields in the current web-based environment. The Smart Health care system plays a most predominant role in the current research for the integration of IoT devices with Edge computing. For secure processing of patient data and test reports, two encryption schemes are applied, namely, Gorti’s encryption scheme and Carmichael’s encryption scheme. LDL values are computed using both the encryption schemes, analysis is performed, and the results are compared. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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Feature Selection by Improved Sand Cat Swarm Optimizer for Intrusion Detection 2

Feature Selection by Improved Sand Cat Swarm Optimizer for I...

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2nd IEEE international conference on Artificial Intelligence in Everything, AIE 2022

作者： Jovanovic, Dijana Marjanovic, Marina Antonijevic, Milos Zivkovic, Miodrag Budimirovic, Nebojsa Bacanin, Nebojsa College of Academic Studies "dositej Belgrade Serbia Singidunum University Technical Faculty Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets-UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration. © 2022 IEEE.

关键词： internet of things

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Edge computing for Brightness and Color Temperature of Smart Streetlight 2

Edge Computing for Brightness and Color Temperature of Smart...

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2nd IEEE international conference on Big Data, Artificial Intelligence and internet of Things Engineering, ICBAIE 2021

作者： Qian, Junhao Duan, Baoyan Xie, Wenjing Zhao, Yixin Southwest University School of Computer and Information Science Chongqing China

ISBN: (纸本)9780738131221

Smart streetlight based IoT system is the best carrier to accelerate the development of smart cities. Generally, the system architecture takes the 'cloud', 'tube', and 'end' centralized structure, easily causing computing resources centralizing, data transmission delay, and network congestion. In this paper, the decentralized edge structure is applied to replace the traditional centralized structure and hence overcome its deficiencies. Firstly, the embedded hardware platform on streetlight edge-side is virtualized to obtain a unified resource pool and two individual virtual machines. Secondly, the two virtual machines are used to establish an edge intelligence management platform and an edge computing service system, respectively. Finally, brightness and color temperature control algorithms are designed by utilizing fuzzy control technology, and are calculated on the obtained computing service system. Experimental results show that our structure and system can efficiently achieve the edge intelligent computing task of brightness and color temperature. © 2021 IEEE.

关键词： Edge computing

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