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Communications and Information technologies (GCCIT), Global Conference on

作者： Satvik Vats C. Vennila Lakshman K S.A. Tiwaskar K. Ranjith Singh Tarun Kapoor Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engg Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A novel way of constructing networks is the software-defined network, or SDN. New network security dangers exist, yet it can better enable dynamic traffic management than the conventional network. It is crucial to use several controllers to promptly identify suspicious SDN devices in order to enhance SDN security and alleviate the burden on a single controller, utilizing analytical modeling of cyber assaults utilizing the concept of topological change in stochastic networks, the paper presents a way to assuring the functionality of Software-Defined Networks (SDN) in cyber attack situations. In contrast to previous popular methods, the suggested technique integrates analytical modeling-based SDN resilience evaluation with neural network-based SDN status monitoring. Analytical expressions may be used to determine the robustness indicators of SDN, which are made feasible by considering the mathematical basis of this evaluation. The suggested major metric for SDN resilience is the right operation coefficient. This study presents an enhanced anomaly detection approach for SDN multi-controller based on the XGBoost mechanism. The system is fault-tolerant, meaning it can withstand aberrant or erroneous instructions and guarantees the right issuance of the flow table. One relatively new machine learning approach that has been gaining a lot of traction is XGBoost. IIt is essential for SDN controllers to have high accuracy in attack detection, low false positive rates, and fast processing speeds. As a detection approach in SDN-based clouds, we use extreme gradient boosting (XGBoost) in this study. Furthermore, we use the POX as the SDN controller, construct the SDN topology using Mininet, and mimic a real-life DDoS assault scenario with the help of the attack tool Hyenae. The XGBoost classifier compares itself to other classifiers using the flow packet data set obtained from TcpDump for DDoS detection. The detection findings show that our technique is faster, more accurate, mo

关键词： Cloud computing Analytical models Accuracy Stochastic processes Process control Robustness Topology Object recognition Software defined networking Resilience

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NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices for Indoor Positioning 8

NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices f...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Wu, Tong Li, Han Gao, Bo Xiong, Ke Fan, Pingyi Ben Letaief, Khaled Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Advanced Network Technologies Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665456128

Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of the existing works focused on mitigating NLOS errors in the scenarios with building structure occlusion, but the NLOS mitigation under human occlusion has not been well addressed. To this end, this paper aims to mitigate the NLOS impact of human occlusion, and then proposes a method of NLOS mitigation to reduce NLOS ranging errors under human occlusion with low-cost UWB devices (i.e., NM-LUD). It takes a set of signal features extracted from low-cost devices as input, calculates their weights and mitigates the ranging error based on the features' weights. Experiment results on the two data sets collected by LinkTrack S and UML1 UWB devices show that the method reduces ranging error from 0.9009 m to 0.1435 m (about by 84.07%) and from 0.7466 m to 0.0792 m (about by 89.39%) respectively, which greatly mitigates the influence of NLOS caused by human occlusion. © 2023 IEEE.

关键词： Costs

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Deep Sequence Models for Text Classification Tasks

arXiv

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arXiv 2022年

作者： Abdullahi, Saheed Salahudeen Yiming, Sun Muhammad, Shamsuddeen Hassan Mustapha, Abdulrasheed Aminu, Ahmad Muhammad Abdullahi, Abdulkadir Bello, Musa Aliyu, Saminu Mohammad Department of Computer Science Kaduna State University Kaduna Nigeria Department of Computer Changchun University of Science and Technology Changchun China Department of Computer Science University of Porto Porto Portugal Department of Computer Science Maharishi University of Management Fairfield United States Department of Computer Science Shehu Shagari College of Education Sokoto Nigeria Department of Computer Science Bayero University Kano Kano Nigeria

The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning;this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans. © 2022, CC BY.

关键词： Long short-term memory

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Online Automobile Rental and E-Marketplace with Augmented Reality (AR)

Online Automobile Rental and E-Marketplace with Augmented Re...

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Vinod, T. Kumaresan, N. Gugan, I. Dhanasekaran, S. Ramprathap, K. Chinnasamy, P. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Dr. N. G. P Institute of Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Information Technology Srivilliputtur India M.Kumarasamy College of Engineering Department of Management Studies Karur India

ISBN: (纸本)9781665461092

The major goal of this research article is to alleviate the suffering of people who need a temporary vehicle or are looking to purchase a new vehicle. Those who do not possess a car, owners of damaged or destroyed automobiles awaiting repair or insurance settlement, or out-of-town travelers, for example. The technology of augmented reality can be utilized to enhance user experience in the process of purchasing new automobiles. Also, being able to add their own cars to put up for sale, can be rewarding to users who want to get rid of their old vehicle easily. © 2022 IEEE.

关键词： Augmented reality

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Fraud Account Detection on Social Network using Machine Learning Techniques

Fraud Account Detection on Social Network using Machine Lear...

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Anupriya, E. Kumaresan, N. Suresh, V. Dhanasekaran, S. Ramprathap, K. Chinnasamy, P. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Dr. N. G. P Institute of Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Information Technology Srivilliputtur India M. Kumarasamy College of Engineering Department of Management Studies Karur India

ISBN: (纸本)9781665461092

Nowadays, a person's impact is frequently determined by the number of followers he or she has on social media. To this aim, the prevalence of false accounts is one of the most pressing issues, with the potential to disrupt a wide range of real-world and economic activity. Bot followers are dangerous to social media as these could alter perceptions of popularity and influence, which can have a ample amount of impact on every sector. As a result, new approaches must be developed to enable the detection and classification of bogus accounts. This study gives novel method for distinguishing original profiles. The method uses information gathered automatically from huge data to characterize typical patterns of fake account. © 2022 IEEE.

关键词： Machine learning

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A Radiation Performance Improvement Method of Extremely Low Frequency Acoustics Promoted Antenna

A Radiation Performance Improvement Method of Extremely Low ...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Qi, Junwei Zhang, Chenwei Li, Yingsong Huang, Zhixiang Loh, Tian Hong Chung, Kwok L. Harbin Engineering University College of Information and Communications Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic and Eletrochemical Technologies Department Middlesex United Kingdom Huizhou University School of Computer Science and Engineering 516007 China

ISBN: (纸本)9798350336740

In this paper, Q-value theory of conventional antennas is used to analyze reasons of low radiation performance of acoustics promoted antennas, where we modify the radiating structure of existing acoustics promoting antenna using the simulations to get the required improvement on radiation performance. The simulation experiments are carried out in the finite element simulation software Comsol Multiphysics to analyze the radiating performance. © 2023 IEEE.

关键词： Antennas

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Big Data Analytics-based Intelligent Logistic System

Big Data Analytics-based Intelligent Logistic System

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Sankar B. Shadaksharappa N. Ganesh Kumar K. Padmanaban Department of Management Studies Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management (GITAM) (Deemed to be University) Bengaluru Karnataka India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India

Due to Internet and e-commerce data growth, logistics management uses big data. Big data’s key elements and technological components are explored, as well as domestic and international research findings, data mining, and data warehouse technologies. In firm logistics management, a logistics center is chosen to use a time series model to estimate transportation freight volume utilizing big data technologies. Big data-based intelligent logistics will improve productivity, cost, and user experience, according to this article. Big data technology may help logistics businesses manage, decide, engage, and allocate resources. Logistics 4.0 uses Industry 4.0 to handle massive volumes of data between equipment, goods, vehicles, and personnel. 5G Mobile Communication Networks might address these needs and boost Smart Logistics growth. This study identifies the primary logistical sectors and activities where 5G may be deployed, stressing its projected benefits and supporting technology for large-scale adoption. The literature review also highlights 5G logistics adoption barriers, suggesting more research.

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PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5690-5704页

作者： Hou, Sizai Li, Songze Jahani-Nezhad, Tayyebeh Caire, Giuseppe Hong Kong University of Science and Technology Hong Kong Southeast University School of Cyber Science and Engineering Nanjing China Supervision and Management (Southeast University) Ministry of Education Engineering Research Center of Blockchain Application China Technische Universität Berlin Department of Electrical Engineering and Computer Science Berlin 10587 Germany

Federated learning (FL), as a promising machine learning paradigm for large-scale distributed data, faces two security challenges of privacy and robustness: the transmitted model updates potentially leak sensitive user information, and the lack of central control over local model updates leaves the global model susceptible to malicious attacks. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) design for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update);and 2) have partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of aggregated statistics of users’ updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines. © 2005-2012 IEEE.

关键词： Byzantine Resilience Federated Learning Privacy Preservation Secure and Robust Aggregation

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Privacy protection and data security in intelligent recommendation systems

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Neural Computing and Applications 2025年 1-18页

作者： Zheng, Donghua Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangdong Guangzhou511363 China School of Graduate Studies Management and Science University Selangor Shah Alam40100 Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Nineveh Mosul41002 Iraq Software Engineering and Digital Innovation Centre Management and Science University Selangor Shah Alam40100 Malaysia

Data security and privacy have emerged as major areas of study for recommendation systems. This is crucial for enhancing system security given the quick advancement of Internet technology and its increased use. However, there are still issues with privacy protection and data security in current intelligent recommendation systems, such as a high probability of privacy leakage, low data security, and poor recommendation performance. To better promote the wider application of intelligent recommendation systems, this article introduces federated learning and differential privacy technology to conduct in-depth research on privacy protection and data security in intelligent recommendation systems. In the article, a federated learning framework is used to construct an intelligent recommendation system model, and the constructed model is trained. Furthermore, a differential privacy mechanism is introduced, which can protect the privacy of samples by analyzing the sensitivity of L1 and L2, gradient descent, gradient pruning, and other methods. Next, the secure multi-party computation (SMC) protocol can be used to achieve model parameter sharing and protect the privacy of participants;subsequently, there is the implementation of federated learning. Finally, this article also evaluates the practical application effects of federated learning and differential privacy technology in privacy protection and data security in intelligent recommendation systems. The study’s findings indicate that the approach suggested in this article has an average data security and privacy leakage risk of 5.35% and 96.64%;federated learning is 16.55% and 85.08%, respectively;differential privacy is 9.65% and 88.47%, respectively;deep learning is 21.08% and 77.92%, respectively;and homomorphic encryption is 10.69% and 82.43%, respectively. The combination of federal learning and differential privacy can effectively protect the privacy and data security of the intelligent recommendation system, reduce

关键词： Sensitive data

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computer Supported Argumentation Learning: Design of a Learning Scenario in Academic Writing by Means of a Conjecture Map 15

Computer Supported Argumentation Learning: Design of a Learn...

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15th International Conference on computer Supported education, CSEDU 2023

作者： Burkhard, Michael Seufert, Sabine Gubelmann, Reto Niklaus, Christina Panjaburee, Patcharin Institute for Educational Management and Technologies University of St.Gallen St. Jakob-Strasse 21 St.Gallen9000 Switzerland Institute of Computer Science University of St. Gallen Rosenbergstrasse 30 St. Gallen9000 Switzerland Faculty of Education Khon Kaen University 123 Thanon Mittraphap Mueang Khon Kaen Thailand

ISBN: (纸本)9789897586415

In academic writing, the competency to argue is important. However, first-year students often have difficulties to construct good arguments. Advances in natural language processing (NLP) have made it possible to better analyze the writing quality of texts. New tools have emerged which can give students individual feedback on their texts and the structure of their arguments. While the use of these argumentation learning support tools can help create better texts, using them in an academic context also carries risks. Learning scenarios are needed that promote argumentation competency using argumentation tools while also making students aware of their limitations. To address this issue, this paper investigates how a learning design with an argumentation learning support tool can be developed to increase the argumentation competency of first-year students. The conjecture-mapping technique was used, to visualize our assumptions and illustrate the developed learning design. As part of a first design cycle, the learning design was tested with 80 students in seven academic writing classes at the University of *** in Switzerland. Preliminary findings suggest that the learning design might be helpful to improve the argumentation competency as well as the data-literacy of students (in relation to argumentation tools). However, further research is necessary to confirm or reject our hypotheses. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Students

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