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Understanding and Developing the Integration of Encapsulating Security Payload in Wireless Network Security

Understanding and Developing the Integration of Encapsulatin...

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Haroon, Syed Jangid, Pradeep Kumar Singh, Jagtej Naveena, S. Sathiya Manisha, P. Singh, Vaishali Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India

ISBN: (纸本)9798350329773

The integration of Encapsulating security Payload (ESP) right into a wireless community safety device calls for careful know-how and improvement. ESP is a crucial security protocol which offers an authentication header, an integrity takes a look at, and encryption of payload information. It really works via encapsulating IP datagram's inside sub-protocols with cryptographic functionality, taking into consideration the secure transport of information throughout the network. At some stage in the mixing system, ESP should be configured such that it could offer the desired degree of security at the same time as meeting performance necessities. This typically entails the configuration of suitable cryptographic algorithms, key sizes, and cryptographic mode settings, as well as other parameters. As soon as integrated, ESP ought to be examined to make sure that it meets the desired requirements. This could be done by using evaluating its overall performance in opposition to a variety of attacks, or by means of going for walks a dedicated penetration testing suite. After success integration and trying out, ESP has to be monitored to detect any changes in its protection posture. Additionally, any recognized issues ought to be remediated in a timely manner to decrease the impact of any safety risk. © 2023 IEEE.

关键词： Integration

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Analyzing the Benefits of Applying Machine Learning to Real-Time Data Analysis 3

Analyzing the Benefits of Applying Machine Learning to Real-...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yadav, Rakesh Kumar Mahesh, T.R. Kumar, Ajay Jayadurga, R. Vishal, A. Sood, Gourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The application of device learning to actual-time data analysis has the potential to revolutionize the way businesses make decisions. With the capacity to procedure big volumes of data fast and correctly, system learning can provide insights that tell strategic decisions at the same time as promoting information-pushed choice making. This evaluation can assist agencies increase efficiency, lower expenses, and create a more aggressive area. By means of leveraging system learning to higher recognize patron behavior, operational operations, and traits in the market, organizations can create proactive procedures to assume customer wishes and capitalize on opportunities. This paper will discover the capacity advantages of making use of device studying to actual-time records analysis and talk how businesses can satisfactory leverage it. The paper can even speak exceptional varieties of device gaining knowledge of strategies and do not forget some problems businesses need to do not forget before integrating system gaining knowledge of into their records evaluation systems. © 2023 IEEE.

关键词： businesses Evaluation exceptional forget records

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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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An Analysis of the Impact of DNS-Based Authentication on Wireless Network Security 1

An Analysis of the Impact of DNS-Based Authentication on Wir...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Gawali, Piyush P. Das, Neeraj Rajesh, A. Shukla, Anupam Sohal, Jagmeet Jayadurga, R. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350359756

This paper investigates the effect of DNS-primarily based authentication on wireless network safety. DNS-primarily based authentication is used to authenticate consumer credentials on Wi-Fi networks, generally primarily based upon the Dynamic Host Configuration Protocol (DHCP). The research specializes in checking out the authentication system via numerous simulations, reading the performance and security of the authentication manner. Effects from such studies monitor that the usage of DNS-based total authentication can improve the safety of a Wi-Fi community, specifically in mitigating man-in-the-center assaults. In addition, analysis shows that DNS-based authentication concurrently improves network overall performance using reducing the fee of authentication, decreasing latency, and increasing the scalability of the authentication process. This research suggests that universal, DNS-based authentication is a precious tool to enhance the security of Wi-Fi networks. © 2023 IEEE.

关键词： Authentication

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Comparing Classification Algorithms to Analyze Twitter Sentiment on Public Opinion on Fuel Oil 24

Comparing Classification Algorithms to Analyze Twitter Senti...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Widarma, Gita Novita, Rice Mustakim Rozanda, Nesdi Evrilyan Universitas Islam Negeri Sultan Syarif Kasim Riau Graduate Program Information Systems Department Pekanbaru Indonesia Postgraduate Programs Universitas Negeri Padang Information Systems Pekanbaru Indonesia Postgraduate Programs Institut Pertanian Bogor Master of Informatics Engineering Pekanbaru Indonesia Postgraduate Programs Universiti Teknologi Malaysia Master of Information Systems Management Pekanbaru Indonesia

ISBN: (纸本)9798350313956

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle Swarm Optimization (PSO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle Swarm Optimization (PSO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%. © 2023 IEEE.

关键词： Decision trees

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Artificial Intelligence Identification Model for Chronic Kidney Disease 1

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5th International Conference on Innovative Computing, IC 2022

作者： Cheng, Ya-Fang Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Many people suffer from Chronic Kidney Disease (CKD). Nowadays, CKD is one of the top ten causes of death. CKD should via invasive examination to understand participants health status. If a non-invasive identification model can be established, it can provide users with self-assessment which let users quickly understand their physical condition. This study used machine learning method to establish an identification model of Chronic Kidney Disease. This study found the associated factors with kidney failure from the literature. Selected MJ database as information resources. Used two different factor selection methods to training model. Compared the performance with K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, eXtreme Gradient Boosting and Vote Algorithms, used the better one to establish the model. In this study, the best model used Vote algorithm to establish the model, and can only use 13 non-invasive factors. The accuracy is 88%, the precision is 73%, the sensitivity is 69%, the specificity is 93%, and the AUC is 0.92. The contribution of this study is to use non-invasive factors to identify Chronic Kidney Disease, but it is a preliminary evaluation and ultimately requires doctors to diagnose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Effective Bandwidth Based Distributed DoS Attack Detection and Source Identification Using Maximum Flow Techniques

Effective Bandwidth Based Distributed DoS Attack Detection a...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： T. Subburaj K. Suthendran Darshan P Deeraj C Jai Shankar M Pushpalatha G Department of Master of Computer Applications Rajarajeswari College of Engineering Bangalore Department of Information Technology Kalasalingam Academy of Research and Education TamilNadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The DDoS attack is one of the most threatening factors affecting the internet services. Now days, most of the common attacks are targeting the gaming industry, online shopping web sites followed by the software and media sectors. Many of the service providers are available to detect and prevent the DDoS attacks. But their software's are not updated enough to detect the recent time attacks. Cyber security challenges include rising ransomware attacks, AI -powered threats, supply chain risks, and frequent data breaches. In the current IoT trend, available DDoS defenses aren't sufficient. This work is aimed to propose a novel BW- DDoS attack detection approach based on maximum data flow identification in networks. The proposed method is faster and taking less number of iterations to determine the maximum data flow and also trace out the source of the attacks with minimum time compared with existing approaches.

关键词： Web and internet services Supply chains Media Denial-of-service attack Market research Software Web sites Ransomware Internet of Things computer crime

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The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset

The Efficient Traffic Sign Detection and Recognition for Tai...

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International Conference on Applied System Innovation (ICASI)

作者： Taweelap Suwattanapunkul Lung-Jen Wang International Master Program in Information Technology and Applications National Pingtung University Taiwan Department of Computer Science and Information Engineering National Pingtung University Taiwan

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results’ comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset.

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices

Random Number Generator for Securing Data Exchange in Smart ...

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computer Technologies (ICCTech), International Conference on

作者： Andi Sama Meyliana Yaya Heryadi Taufik Roni Sahroni Computer Science Department Binus Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

IT/OT convergence in the form of a Cyber-Physical System (CPS) presents an opportunity to apply recent information Technology (IT) advancements to Operational Technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption.

关键词： Industries Systematics Market research Data transfer Control systems Generators Encryption

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