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2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)最新文献

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QoS based Security Architecture for Software- Defined Wireless Sensor Networking 基于QoS的软件定义无线传感器网络安全体系结构
Himanshi Babbar, Shalli Rani, Sardar M. N. Islam, S. Iyer
An innovative QoS based security architecture for software-defined wireless sensor networking is developed in this paper. Conventional networks are constrained by hardware limitations and rigid architectural design, limiting research and innovation. Software-Defined Networking (SDN) is a networking breakthrough that allows administrators to control and customize their entire network from a central location. SDN makes network management more manageable and more effective. SDN model can deliver versatile routing and help the various communication patterns found in Wireless Sensor Networks (WSN). However, adopting this model to resource- constrained networks is difficult, particularly when security services are required. Current Software-Defined Networking- based Wireless Sensor Networks (SDN-WSN) methods have developed over time to meet resource-constrained needs. In this paper, the authors have discussed security architecture, its challenges and solutions. Further, the comparative analysis of various security algorithms (SEC-SDWSN, Genetic, Routing optimization for cross-domain preservation (ROCDP)) in SDN- WSN is carried out. The SEC-SDWSN achieved the better results based on the Quality of Service (QoS) metrics (packet delivery ratio, data transmission, response time and energy consumed parameters) which shows packet delivery ratio in SEC-SDWSN is 4% better than Genetic and ROCDP; data transmission rate in SEC-SDWSN is 15Kbps better than Genetic; and response time in SEC-SDWSN is 5% better than Genetic. Therefore, an original contribution to the literature and practices of network security architecture is made in this paper by developing an innovative QoS based security architecture for software-defined wireless sensor networking.
提出了一种创新的基于QoS的软件定义无线传感器网络安全体系结构。传统的网络受到硬件限制和僵化的架构设计的限制,限制了研究和创新。软件定义网络(SDN)是一项突破性的网络技术,它允许管理员从一个中心位置控制和定制整个网络。SDN使网络管理更易于管理,更有效。SDN模型可以提供多用途路由,并支持无线传感器网络(WSN)中的各种通信模式。然而,在资源受限的网络中采用这种模型是困难的,特别是当需要安全服务时。当前基于软件定义网络的无线传感器网络(SDN-WSN)方法是随着时间的推移而发展的,以满足资源受限的需求。在本文中,作者讨论了安全架构,其挑战和解决方案。进一步,对SDN- WSN中各种安全算法(SEC-SDWSN、Genetic、Routing optimization for cross-domain preservation (ROCDP))进行了比较分析。基于服务质量(QoS)指标(分组传送率、数据传输、响应时间和能耗参数),SEC-SDWSN的分组传送率比Genetic和ROCDP高4%;SEC-SDWSN的数据传输率比Genetic提高了15Kbps;与遗传算法相比,SEC-SDWSN的响应时间提高了5%。因此,本文通过为软件定义无线传感器网络开发一种创新的基于QoS的安全架构,对网络安全架构的文献和实践做出了原创性的贡献。
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引用次数: 2
Data security and privacy in cloud computing focused on transportation sector with the aid of block chain approach 基于区块链技术的交通运输领域云计算数据安全与隐私研究
Abhiyan Gurung
Despite the exponential growth of cloud infrastructure over several years, data protection and reliable computing are still major obstacles for modern cloud computing applications. In order to address this issue, several researchers have done a lot of work on this and have suggested a variety of models, including data integrity checking and stable multi-party estimates. Vehicle ad-hoc networks (VANETs) have been diagnosed with several security issues, such as confidentiality, safe authorization/authentication, and system stability. Nevertheless, almost all these alternatives face challenges like over-computational complexity or absence of scalability. This paper explores the usage of blockchain technologies in order to strengthen this scenario. Blockchain is collaborative modern framework for distributed computation. Applying blockchain technologies to cloud infrastructure, leveraging the former encryption framework to boost the efficiency of data storage and decentralized computation, is an exciting research area. A system of message verification for the privacy and decentralization of knowledge utilizing blockchain technologies is proposed in this paper. This is where we add message authentication code (MAC) and public-private key for stable authentication. In this document, we follow consensus algorithms for computing blockchain structures such as proof of work (PoW) and Practical Byzantine Fault Tolerance (PBFT) in the proposed authorization method. Finally, it is shown that the suggested approach is secure from threats that require the impersonation of the internal intruder as well as the usual threats.
尽管云基础设施在过去几年中呈指数级增长,但数据保护和可靠计算仍然是现代云计算应用的主要障碍。为了解决这个问题,一些研究人员在这方面做了大量的工作,并提出了各种模型,包括数据完整性检查和稳定的多方估计。车辆自组织网络(vanet)已被诊断出存在几个安全问题,例如机密性、安全授权/身份验证和系统稳定性。然而,几乎所有这些替代方案都面临着过度计算复杂性或缺乏可伸缩性等挑战。本文探讨了区块链技术的使用,以加强这种情况。区块链是分布式计算的现代协作框架。将区块链技术应用于云基础设施,利用以前的加密框架来提高数据存储和分散计算的效率,是一个令人兴奋的研究领域。本文提出了一种利用区块链技术实现知识隐私和去中心化的消息验证系统。我们在这里添加消息身份验证码(MAC)和公私钥,以实现稳定的身份验证。在本文档中,我们在提议的授权方法中遵循共识算法来计算区块链结构,如工作量证明(PoW)和实用拜占庭容错(PBFT)。最后,表明建议的方法是安全的,不受需要模拟内部入侵者和通常威胁的威胁。
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引用次数: 0
Using Deep learning for network traffic prediction to secure Software networks against DDoS attacks 利用深度学习进行网络流量预测,保护软件网络免受DDoS攻击
D. T. Sulaga, Angelika Maag, Indra Seher, Amr Elchouemi
Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches.
深度学习(DL)是一项新兴技术,由于其有效性而被应用于许多领域。它的主要应用之一是攻击检测和防止后门攻击。在物联网(IoT)网络的软件定义网络中,基于采样的测量方法通常会导致精度低、开销高、内存消耗高和攻击检测低。本研究旨在回顾和分析安全软件网络中针对分布式拒绝服务攻击(DDoS)问题的基于dl的网络预测技术的论文。研究了能够有效预测网络流量和检测DDoS攻击的技术和方法。在此回顾的基础上,确定了每个工作中的主要组件,并据此建议了显示所需基本过程的整体系统架构。主要的发现是,DL比其他先进的方法更有效地对抗DDoS攻击。
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引用次数: 0
Review of existing variants of Grey Wolf Optimization algorithm handling Load Balancing in Clouds 灰狼优化算法在云中处理负载平衡的现有变体综述
Suman Sansanwal, Nitin Jain
Cloud computing has earned lot of awarenes in the Information Technology and now appeared as the next level in the evolution of Internet. But now it has been observed that the Load balancing (LB) is one among various challenges in cloud computing that needs to be resolved to perform the accurate operations on cloud and also to obtain the rapid development in the the sphere of cloud computing. The demand of various customers all over the world for the services used to increase the load rapidly. Therefore load balancing required to equally distribute the workload over each and every virtual machine in the cloud system hence as a result the throughput increases and the response time minimizes. The aim of load balancing is to build the client satisfaction, resource utilization maximisation and improvement in the cloud system performance leads to reduction in energy consumption and heat dissipation. In the present paper, the standard Grey Wolf Optimisation algorithm for load balancing is demonstrated for the cloud environment. Also the other versions of Grey wolf optimisation has been studied to know the issues related to them and additional functionality required by them to achieve the higher system performance. Furthermore, according to the surveyed research papers it has been seen that now the performance of the proposed hybrid Grey Wolf Optimisation algorithms is simulated by using Cloudsim simulator on the basis of different parameters such as throughput and response time etc.
云计算已经在信息技术领域获得了广泛的关注,现在已经成为互联网发展的下一个阶段。但是,负载平衡(Load balancing, LB)问题已经成为云计算中实现准确的云计算操作,并在云计算领域获得快速发展所需要解决的难题之一。世界各地各种客户对所使用服务的需求使负载迅速增加。因此,负载平衡需要在云系统中的每个虚拟机上均匀地分配工作负载,因此吞吐量增加,响应时间最小化。负载均衡的目的是建立客户满意度,最大限度地利用资源,提高云系统的性能,从而减少能耗和散热。在本文中,标准的灰狼优化算法的负载平衡演示了云环境。此外,我们还研究了灰狼优化的其他版本,以了解与它们相关的问题以及它们所需的附加功能,以实现更高的系统性能。此外,根据调查的研究论文,已经看到现在提出的混合灰狼优化算法的性能是通过使用Cloudsim模拟器基于不同的参数,如吞吐量和响应时间等进行模拟。
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引用次数: 0
Conference Committee for CITISIA 2021 2021年CITISIA会议委员会
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引用次数: 0
Topic: Scoping review of Blockchain based data storage technique in industrial IoT data management 主题:基于区块链的数据存储技术在工业物联网数据管理中的范围综述
A. Aanchal, P. Prasad
The review study was based on the integrated network and the secured protocols and blockchain technology to detect security issues and protect the user’s data storage. The study aims to review the recently published research articles that state the data storage techniques and blockchain technology for the IIoT to manage data confidentiality. The method opted here was the literature review for the collections and knowledge of the data management. Moreover, a secondary research method was selected in which the currently published research article was gathering to review the literature. The research paper's expected finding was that data storage and data management are the two crucial factors that had been used within the industry for allocation and work management. It had helped in enhancing industrial performance. The research work's contribution is in data collection from different libraries based on data storage techniques. This work plays a huge contributory role in investigating the currently available solutions dependent on blockchain technology and data storage technique for managing the data effectively and delivering valuable insights for data storage capabilities. The review study included different factors and attributes classified by considering different instances as per the author’s interest. The major component was also evaluated in this study through a different segment, including error rate and accuracy matrix that shows the system validation. The study verifies the system by considering different terms and their related frequency in the last segment, which justifies system accuracy.
审查研究基于集成网络和安全协议以及区块链技术,以检测安全问题并保护用户的数据存储。该研究旨在回顾最近发表的研究文章,这些文章阐述了工业物联网管理数据机密性的数据存储技术和区块链技术。这里选择的方法是文献综述的收集和知识的数据管理。此外,我们选择了一种二次研究方法,即收集当前发表的研究文章来回顾文献。该研究报告的预期发现是,数据存储和数据管理是行业内用于分配和工作管理的两个关键因素。它有助于提高工业绩效。本研究工作的贡献在于基于数据存储技术从不同的图书馆收集数据。这项工作在研究依赖于区块链技术和数据存储技术的当前可用解决方案方面发挥了巨大的贡献作用,这些解决方案可以有效地管理数据,并为数据存储能力提供有价值的见解。综述性研究包括不同的因素和属性,根据作者的兴趣考虑不同的实例进行分类。本研究还通过不同的部分对主要成分进行了评估,包括错误率和显示系统验证的准确性矩阵。在最后一段考虑不同项及其相关频率对系统进行了验证,验证了系统的准确性。
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引用次数: 0
DNA based service data security in cloud computing environment 云计算环境下基于DNA的服务数据安全
Amish Patel, Zhongyan Ge, Indra Seher, Van Luong Vo
Cloud computing is the type of technology preferred to maintain computing power, computer resources, and majorly used to handle the cloud storage process. Also, it helps to manage the data security process during different applications. The data encryption, data classification, and feature extraction approaches are majorly considered for increasing the data security process. The main aim of the work is to review the DNA based cloud computing process for improving data security. The main purpose of this work is to review the current research article focus on the data security and computing process. Further, taxonomy is introduced that helps in the evaluation and analysis process. The secondary research approach is used during review work. Further, the deep learning with sequencing and modification process is reviewed in the work to manage the security and sequence analysis process. In addition, the block-chain-based random technique is reviewed to manage data detection and probability rate. The data optimization and data indexing process with block-chain is preferred to enhance data security and handle the feasibility, accuracy, and transfer time. Moreover, the OPNET model, Dynamic and static model approach, and data access algorithms are used together for maintaining the data mining security process. The IoT network topology and attributes based encryption process are reviewed in developed methods for maintaining data optimization and data security process. These approaches and factors are analyzed with the help of current research articles in order to review the state of the art in the developed method.
云计算是维护计算能力、计算机资源的首选技术类型,主要用于处理云存储过程。此外,它还有助于管理不同应用程序期间的数据安全过程。数据加密、数据分类和特征提取是提高数据安全性的主要方法。这项工作的主要目的是审查基于DNA的云计算过程,以提高数据安全性。本工作的主要目的是回顾当前的研究文章集中在数据安全和计算过程。此外,还介绍了有助于评估和分析过程的分类法。在审查工作中使用二次研究方法。在此基础上,综述了基于排序和修改过程的深度学习在安全性和序列分析过程管理中的应用。此外,回顾了基于区块链的随机技术对数据检测和概率率的管理。首选区块链数据优化和数据索引过程,以增强数据安全性,处理可行性,准确性和传输时间。同时,采用OPNET模型、动态和静态模型方法以及数据访问算法来维护数据挖掘过程的安全性。回顾了物联网网络拓扑结构和基于属性的加密过程,并提出了维护数据优化和数据安全过程的方法。本文结合当前的研究文章对这些方法和因素进行了分析,以回顾所开发方法的最新进展。
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引用次数: 0
The Implementation of Low RPM Generator on Small Scale Savonius Vertical Axis Wind Turbine (VAWT) 低转速发电机在小型Savonius垂直轴风力机上的实现
Fahmi Adam Augusta, Ahmad Husin Lubis, A. Syahriar, Putri Wulandari
Fossil energy, especially petroleum, is the main energy source and a source of foreign exchange. Indonesia has a limited amount of fossil energy. Meanwhile, energy consumption continues to increase along with the economic and population growth. Thus natural resources such as oil, gas, and coal are becoming more depleted, because they are not renewable energy. Indonesia is a tropical country located on the equator, as an archipelago with varied geological contours, more than 100 mountains, and also beaches. One of the energies that might be utilized is wind energy with an affordable cost and free pollutant output. Wind Turbines have become one of the feasible power plants to replace fossil energy. The mechanism of wind turbine is that the wind blows against the blades of the wind turbine, and allows the blades to rotate as the axis and produces a valuable source of renewable energy by repetitive rotational motion. This research was focused on finding a suitable generator for Savonius Vertical Axis Wind Turbine (VAWT) that can produce a high voltage at low RPM. In this research, to replicate the real wind blow, two different fans are used: a. 71 cm diameter industrial fan with range of wind speed from 1 to 6 m/s; b. 31 cm fan with a range of wind speed from 1 to 4,5 m/s. From the result, it can be concluded that the direction of the wind affects the rotation of the rotor, so it must be ensured that the wind touches the tip of the blade to maximize the rotational speed. The maximum voltage output produced by VAWT is 4,8 V at 67 RPM on wind speed 6 m/s using the 1st Generator. For further development, the Vertical Axis Wind Turbine (VAWT) have to be remodelled so the ratio between rotor radius and rotor height is 1,2.
化石能源,特别是石油,是主要的能源和外汇来源。印尼的化石能源有限。与此同时,随着经济和人口的增长,能源消耗也在不断增加。因此,石油、天然气和煤炭等自然资源正变得越来越枯竭,因为它们不是可再生能源。印度尼西亚是一个位于赤道上的热带国家,是一个拥有不同地质轮廓的群岛,有100多座山脉和海滩。其中一种可以利用的能源是风能,它的成本低廉,而且不会产生污染物。风力发电机组已成为替代化石能源的可行动力装置之一。风力发电机的工作原理是,风吹在风力发电机的叶片上,使叶片以轴为轴旋转,通过反复的旋转运动,产生有价值的可再生能源。这项研究的重点是为Savonius垂直轴风力涡轮机(VAWT)寻找一种合适的发电机,可以在低转速下产生高电压。在这项研究中,为了复制真实的风吹,使用了两种不同的风扇:a.直径71厘米的工业风扇,风速范围为1至6米/秒;B. 31厘米风机,风速范围为1 ~ 4.5米/秒。从结果可以得出,风的方向影响转子的转动,因此必须保证风接触到叶片的尖端,以最大限度地提高转速。VAWT产生的最大电压输出为4,8 V, 67 RPM,风速6米/秒,使用第一台发电机。为了进一步发展,垂直轴风力涡轮机(VAWT)必须改造,使转子半径与转子高度之间的比值为1,2。
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引用次数: 0
Early Detection of Under-Performing Students Using Machine Learning Algorithms 使用机器学习算法早期发现表现不佳的学生
Khalid Alalawi, R. Chiong, R. Athauda
Predicting student performance and identifying under-performing students early is the first step towards helping students who might have difficulties in meeting learning outcomes of a course resulting in a failing grade. Early detection in this context allows educators to provide appropriate interventions sooner for students facing challenges, which could lead to a higher possibility of success. Machine learning (ML) algorithms can be utilized to create an early warning system that detects students who need assistance and informs both educators and learners about their performance. In this paper, we explore the performance of different ML algorithms for identifying under-performing students in the early stages of an academic term/semester for a selected undergraduate course. First, we attempted to identify students who might fail their course, as a binary classification problem (pass or fail), with several experiments at different times during the semester. Next, we introduced an additional group of students who are at the borderline of failing, resulting in a multiclass classification problem. We were able to identify under-performing students early in the semester using only the first assessment in the course with an accuracy of 95%, and borderline students with an accuracy of 84%. In addition, we introduce a student performance prediction system that allows academics to create ML models and identify under-performing students early on during the academic term.
预测学生的表现,及早发现表现不佳的学生,是帮助那些可能难以达到课程学习成果而导致成绩不及格的学生的第一步。在这种情况下,早期发现使教育工作者能够更快地为面临挑战的学生提供适当的干预措施,从而提高成功的可能性。机器学习(ML)算法可以用来创建一个预警系统,检测需要帮助的学生,并告知教育工作者和学习者他们的表现。在本文中,我们探讨了不同ML算法的性能,用于识别在一个学期/学期的早期阶段表现不佳的学生。首先,我们试图识别可能不及格的学生,作为一个二元分类问题(及格或不及格),在学期的不同时间进行了几次实验。接下来,我们引入了另外一组处于不及格边缘的学生,这导致了一个多类分类问题。我们能够在学期早期仅使用课程的第一次评估就识别出表现不佳的学生,准确率为95%,而边缘学生的准确率为84%。此外,我们还引入了一个学生成绩预测系统,该系统允许学者创建机器学习模型,并在学期早期识别表现不佳的学生。
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引用次数: 0
Customer data extraction techniques based on natural language processing for e-commerce business analytics 基于自然语言处理的电子商务业务分析客户数据提取技术
Abdul B. Maqsood, Angelica Maag, Indra Seher, Md Sayfullah
Natural language processing (NLP) is the a types of artificial intelligence approach used to maintain the decision making and data interaction process with high accuracy and reliability rate. It is also used to maintain the computer-human interaction for better understanding and result. The aim of this work is to review the data extraction techniques with NLP for a better business and user analysis process. For data analysis and user experience analysis process data analytic, K-neighbor techniques are used that are obtained using the method a lLiterature review. This process aims to review the current research articles that are focused on data extraction and analytic techniques. Besides, it is focused on NLP techniques for improving the analysis and extraction process. The Factorization, FCMA, and soft computing algorithms with NLP are reviewed that maintain precision and accuracy rate. Different tools, such as visualization, decision-making, consumer identification, and behavior analysis, are considered during the review process. In this review process, PRM and embedding matrix approaches are considered for an accurate analysis process. The data extraction, feature extraction, and machine learning model with data extraction techniques are reviewed to manage consumer experience and error estimation. This study introduces customer behavior data, Natural processing-based data extraction, e-commerce business effectiveness and evaluation as the major factors of this work.
自然语言处理(NLP)是一种人工智能方法,用于维持决策和数据交互过程的高精度和可靠性。它还用于维护人机交互,以便更好地理解和结果。这项工作的目的是回顾NLP的数据提取技术,以便更好地进行业务和用户分析过程。对于数据分析和用户体验分析过程数据分析,使用k邻居技术,该技术是通过文献综述的方法获得的。这个过程的目的是回顾当前的研究文章,集中在数据提取和分析技术。此外,重点介绍了用于改进分析和提取过程的自然语言处理技术。本文综述了基于自然语言处理的因子分解、FCMA和软计算算法,这些算法都能保持精度和正确率。不同的工具,如可视化、决策、消费者识别和行为分析,在审查过程中被考虑。在这个回顾的过程中,考虑到PRM和嵌入矩阵的方法,以准确的分析过程。回顾了数据提取、特征提取和带有数据提取技术的机器学习模型,以管理消费者体验和误差估计。本研究将顾客行为数据、基于自然处理的数据提取、电子商务业务有效性及评价作为本工作的主要因素。
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引用次数: 0
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2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)
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