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A Routing Algorithm Based on Software-Defined computing First Network 3

A Routing Algorithm Based on Software-Defined Computing Firs...

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2023 3rd international conference on Electronic Communication, Computer Science and technology, ECCST 2023

作者： Bai, Chenchen Gong, Xiaoming Ren, Shuangyin Xu, Tianji Wang, Jingchao University of Electronic Science and Technology of China Chengdu China Institute of Systems Engineering AMS PLA Beijing China

With the continuous development of mobile internet technology, new services and applications appear, such as AR/VR, internet of Vehicles, and Ultra HD Video. They not only require higher network bandwidth resources but also need more computing resources to ensure normal operation and user experience. As a new network technology, computing first network promotes the deep integration of distributed computing resources and networks to make efficient use of these resources. This paper proposes a computing power routing algorithm based on software-defined computing first network (SD-CFN) architecture. It comprehensively considers computing resources and available bandwidth resources of network links. Experimental results show that compared with the nearby scheduling strategy, the proposed computing power routing algorithm can better allocate computing resources to services. Moreover, it can improve the utilization rate of computing resources and provide optimal bandwidth resources in the process of service transmission. © Published under licence by IOP Publishing Ltd.

关键词： Routing algorithms

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Data Analysis and Decision Support Effect of Big Data Mining in Energy internet Cyber-Physical Systems 3

Data Analysis and Decision Support Effect of Big Data Mining...

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3rd international conference on Electronic Informationtechnology and Smart Agriculture, ICEITSA 2023

作者： Zheng, Kangzhen Qi, Hongtao Ni, Yanyan Tang, Xu Marketing Service Center State Grid Anhui Electric Power Co. Ltd. Anhui Hefei China

ISBN: (纸本)9798400716775

The construction of energy internet can realize the massive collection of load-side data, and the local utilization of load-side data through edge computing devices can effectively reduce the pressure of cloud computing and communication system, but the computing and solving ability of edge devices is worse than that of cloud, and too many computing tasks of edge devices may also affect the overall scheduling. Therefore, this paper combines big data mining technology to analyze the data analysis and decision support in the cyber-physical systems of energy internet, and studies the optimal scheduling method of energy internet in cloud edge environment around the multi-energy complementary process and accurate demand response of energy internet. The research results show that the energy internet information model based on information physical fusion takes into account the plug-and-play characteristics of distributed devices in the network, and at the same time reflects the multi-time scale characteristics of energy internet, taking into account the autonomy of specific devices in the network and so on. From the experimental evaluation, it can be seen that big data mining has a good effect on data analysis and decision support in energy internet cyber-physical systems. © 2023 ACM.

关键词： internet of Energy

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Denial of Service Attack Detection and Mitigation using Ensemble based ML in Software Defined Network 3

Denial of Service Attack Detection and Mitigation using Ense...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Sathish, N. Valarmathi, K. Nagalakshmi, R. Hema Priya, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College India Srm Institute of Science and Technology Computer Science and Engineering. Ramapuram India Easwari Engineering College Department of Cse India

ISBN: (纸本)9798350375190

Software-defined networking (SDN) introduces a novel networking paradigm by decoupling control logic from forwarding operations, resulting in improved network resource management and administration efficiency. However, centralized network design in SDN gives rise to significant security challenges, notably denial-of-service (DoS) attacks. Attackers exploit SDN's lack of effective message-verification mechanisms to launch DoS attacks by falsifying source address information. In response, this study proposes an innovative approach to internet of Things (IoT) security leveraging software-based networks and collaborative learning. One key strategy involves partitioning the network domain into smaller subdomains, each with its own controller, facilitating the exchange of security rules among subdomains. However, this topology necessitates routing every packet through the subnet's control node. To continually monitor network traffic data and detect attacks, each controller node employs an integrated learning model. This model combines statistical insights from data streams with artificial neural networks and the Extreme Gradient Boosting Algorithm to anticipate potential attacks. Additionally, the ICOA model selects relevant characteristics for analysis. Through comprehensive performance evaluation, including empirical values, the ensemble model demonstrates superior performance compared to existing approaches. Furthermore, the study compares various models and methodologies, affirming the efficacy of the proposed strategy in accurately identifying distributed Denial-of-Service (DDoS) attacks in SDNs, thereby presenting a groundbreaking approach to SDN security. © 2024 IEEE.

关键词： Neural networks

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Advancing IoT Security through Federated Learning: A Comprehensive Approach 3

Advancing IoT Security through Federated Learning: A Compreh...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Mehta, Shiva Khurana, Meenu Dogra, Ayush Hariharan, Shanmugasundaram Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350375190

IoT devices have changed into one of the mainstays in areas like houses, industry and education, resulting in a level of convenience and efficiency that has never been achieved before. Even though the most important advantage of the distributed network is raising the level of security, the problem of security vulnerabilities with the extended networks remains. This paper is devoted to a novel strategy of Federated Learning (FL) that could become an applied solution for enhancing the safety of the IoT. Therefore, based on its decentralized knowledge process, FL allows IoT devices to cooperate in achieving common purposes without endangering data pri vacy. The paper uses this thinking method to deal with scalability, model personalization, and non-IID data that aren't IID distributing, suggesting solutions through model aggregation techniques or Artificial Intelligence (AI). Experimental evidence highlights that FL is very useful in enhancing IoT security irrespective of the diversity of its applications. To this end, residents of smart homes experienced a 17% increase in detecting the anomalies through the setting up of FL. This situation was replicated in the industrial internet of Things, where the false positive rates were reduced from 20% to 5%, thus improving the system's efficiency. Furthermore, in the FL context in healthcare IoT ecosystems, this blazes a trail for privacy-preserving data analytics, preserving patient confidentiality and systematic profiling of noteworthy health trends. These findings thus demonstrate that FL is a feasible way of dealing with the security challenges of IoT scalable, promptly and in a manner that maintains user privacy. The article takes part in a conversation about ways to strengthen the security of IoT networks. Such a framework is based on the computational power of IoT devices. It allows us to detect and prevent threats in time. Nonetheless, it urges extensive R&D attempts to be introduced to develop better security solutio

关键词： internet of things

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Enhancing Task Scheduling in Cloud computing: A Multi-Objective Cuckoo Search Algorithm Approach 3

Enhancing Task Scheduling in Cloud Computing: A Multi-Object...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Shetty, Pallavi Veeraiah, Vivek Khidse, Swati Vitthal Rai, Mritunjay Gupta, Ankur Dhabliya, Dharmesh Dept. of Mca Karnataka Udupi India Sri Siddhartha Academy of Higher Education Dept. of Computer Science Karnataka Tumkur India Dr. Babasaheb Ambedkar Marathwada University Maharashtra Aurangabad India Shri Ramswaroop Memorial University Dept. of Ece UP Barabanki India Vaish College of Engineering Dept. of Cse Haryana Rohtak India Vishwakarma Institute of Information Technology Dept. of It Maharashtra Pune India

ISBN: (纸本)9798350330724

Services that are delivered and managed online employ cloud computing. Without taking into account the end user's actual location, cloud computing offers data access and storage devices. Clouds are quickly becoming as a crucial application platform for science. Due to the limitless number of nodes in the cloud environment, resources are inherently unstable, which has a significant impact on task execution and scheduling. Hardware, virtualization, distributed computing, and internet-based service delivery improvements in recent years have all contributed to the evolution of cloud computing. Cloud computing may not entail many cutting-edge technology, but it does offer a novel approach to IT management. An essential and crucial issue in cloud systems is the proper scheduling of jobs that have been received across service providers. In cloud settings, the scheduling problem is NP-hard. The current system offers a scheduling algorithm that takes care of the main problems with job scheduling in the cloud. To plan the jobs in cloud computing for this project, the system suggested a method called Multi Objective Cuckoo Search. © 2023 IEEE.

关键词： Scheduling algorithms

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Analyzing Resource Allocation Methods in Fog computing for Task Scehduling: A Study of Heuristic and Meta-Heuristic Approaches 3

Analyzing Resource Allocation Methods in Fog Computing for T...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Himanshu Mangla, Neeraj University Department of Computer Science and Engineering Ambala Mullana India

ISBN: (纸本)9798350343632

With the remarkable improvement of the internet, there has been an important flow in technologies like cloud and fog computing. Moreover, it is important to identify the associated issues related to resource allocation and deployment in these technologies. Despite the rapid development of cloud computing (CC), several limitations persist due to inherent features like unreliable delays, limited mobility, and limited support. Fog computing (FC), on the other hand, includes taking the central CC resources nearby to the network edge. It aims to overcome the limitations faced by CC by providing a more distributed method compared to its centralized behavior. The disparities between compute-intensive uses and resource-constrained devices challenge the complete system's performance. Task scheduling (TS) is important in identifying logical inconsistencies in fog technology. With the proliferation of the IoT, efficiently allocating resources to clients upon request becomes a highly challenging task in fog technology. Different authors survey several methods, which are analyzed in brief. Lastly, a study is made by comparing the demerits of each technique, and recommendations are defined for improved TS and RA in fog. In this article, some heuristic and metaheuristic methods are studied, and the hyper-heuristic scheduling (HHS) method is defined to explore the best distribution with minimum execution time. The implemented technique has enhancements in energy, execution time, etc. © 2023 IEEE.

关键词： Cloud computing (CC) Fog computing (FC) Hyper-heuristic scheduling (HHS) Resource Allocation (RA) Task Scheduling (TS).

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Utilizing internet of Things in Intelligent City, and Medical Sharing technology 3

Utilizing Internet of Things in Intelligent City, and Medica...

引用

3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Gandhi, Ankita Tripathy, Padmaja Parul University PIET Department of CSE India ARKA JAIN University Department of Engg. & IT India

ISBN: (纸本)9798350399264

Due to the unsatisfactory national public hospital management system, excessive medical costs, a lack of channels, and some other issues affecting people's ability to support themselves. The focus of public concern is on medical issues, namely those represented by "an inadequate medical system, subpar medical services, healthcare challenges, and exorbitant expenses."The main objective of this article is to develop a set of smart health data network platform technologies that will enable patients to receive elevated treatment and diagnosis options with low waiting times and expenses.. To properly accomplish "everyone's heath, everyone's health,"that issue of "difficult and costly medical treatment"must be resolved. This project develops and designs a system for distributed health information sharing and integration. Then, a few premier local hospitals adopt it. These results of the poll and interviews research show that the healthcare field has substantially improved and that smart medicine distribution has increased by roughly 79 percent among general population compared to earlier medical treatments. © 2023 IEEE.

关键词： Hospitals

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The Application of Transfer Learning to the Detection of Signals in Wireless Networks 3

The Application of Transfer Learning to the Detection of Sig...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Saket Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

In this research, we provide a machine learning strategy for detecting flaws during production runs. As an example of a scenario where sensors are spread out over a large industrial plants, the Tennessee Eastman System is employed. A filter-wrapper hybrid feature choosing approach called the Hybrid Fisher Wrapper approach was created to choose the most representative sensors to achieve the greatest detecting quality for fault detection. The proposed method provides a wide variety of options, each with its own level of sensing difficulty, computational load, and detectability. It's an alternative to the conventional Industry 4.0 strategy, in which a distributed network of sensors sends data to a consolidated cloud. The proposed solution is decentralized compared to centralized servers so that processing may take place close to the sensors that produce the data or at the periphery of the internet of Things. The connectivity, privacy, and latency of this technique are all better than those of centralized alternatives. The experimental results show that the proposed method produces fault identification solutions that are at par with or better than state-of-the-art. The produced solutions are 37.5 times faster and require 1.99 times fewer characteristics than performing with the highest detection quality. The scalability of the framework provides an architectural framework from which the best possible implementation may be chosen. © 2023 IEEE.

关键词： Edge computing Fault Detection Feature Selec tion Industry 4.0 Machine Learning

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An IoT Device Recognition Method based on Convolutional Neural Network 3

An IoT Device Recognition Method based on Convolutional Neur...

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3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Lu, Minghao Gao, Yali Li, Linghui Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350335972

Device recognition is the primary step toward a secure IoT system. However, the existing equipment recognition technology often faces the problems of unobvious data characteristics and insufficient training samples, resulting in low recognition rate. To address this problem, a convolutional neural network-based IoT device recognition method is proposed. We first extract the background icons of various IoT devices through the internet, and then use the ResNet50 neural network to extract icon feature vectors to build an IoT icon library, and realize accurate identification of device types through image retrieval. The experimental results show that the accuracy rate of sampling retrieval in the icon library can reach 98.5%, and the recognition accuracy rate outside the library can reach 83.3%, which can effectively identify the type of IoT devices. © 2023 IEEE.

关键词： Convolutional neural networks

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An Intense Systematic Review of Mobile Agent Based Harmful Threats and Attack Detection Systems in IOT Networks 3

An Intense Systematic Review of Mobile Agent Based Harmful T...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Vigenesh, M. Ravivarman, G. Singh, Prabhdeep Vaseem Akram, Shaik Sharma, Deepti Tiwari, Mohit Karpagam Academy of Higher Education Department of CSE Coimbatore India Karpagam Academy of Higher Education Department of EEE Tamil Nadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India Uttaranchal University Department of Management Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350399264

A breach is detected by an intrusion detection system (IDS) by analysing events that are deemed risky from a security standpoint in systems and networks. Due to a surge in occurrences involving network security, organisations must implement IDS for the protection of sensitive data. Both intrusions coming from outside a network and those coming from within a distributed network are challenging to identify. IDS should be in charge of processing a sizable volume of data without overwhelming networks and monitoring systems. Due to the flaws and restrictions in centralised IDS, mobile agents (MA) were developed. By identifying harmful activity, these agents can carry out specified actions. From earlier works of literature, it was deduced that most of the existing IDS based on MA are not significantly effective © 2023 IEEE.

关键词： internet of things

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