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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Venkateswaran, R. Babu, B.N.V. Madhu Parameswari, M. Vamsilala, Purushothapatnapu Naga Venkata Ambigaipriya, S. Keerthana, N.V. College of Computing and Information Sciences University of Technology and Applied Sciences Cyber and Information Security Salalah Oman Teegala Krishna Reddy Engineering College Department of Cse Telangana Hyderabad India Sona College of Technology Department of Information Technology Tamilnadu India Vignan's Lara Institute of Technology and Science Andra Pradesh Vadlamudi Guntur India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Trichy India Velalar College of Engineering and Technology Department of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350380583

The most crucial public concern regarding health is elderly people's falls which tend to undesirable injuries leading to an increase in healthcare expenses for them. This research paper describes a method that uses AI technology with a barometer sensor to detect falls in elderly people. In addition to this, the AI model is trained on a diverse dataset comprising various activities of daily living, enabling it to distinguish between normal movements and those that may lead to falls. This method probably gives some notifications or missed calls to the caretakers as well as to the hospitals. When compared to many more sensors such as gyroscopes and some fitness trackers, the Apple Watch 4, and Samsung Gear S3 barometer sensor perform in a better better way. Key performance metrics such as absolute and altitude are calculated and compared based on a natural language processing (NLP) algorithm. © 2024 IEEE.

关键词： Barometers

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Image Classification Using Federated Learning 6th

Image Classification Using Federated Learning

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Byeon, Haewon Ullah, Ajdar Syed, Ziauddin Siddiqui, Alighazi Aftab, Aasif Sarfaraz, Mohd Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of School of Mechanical and Manufacturing Engineering National University of Science and Technology Islamabad44000 Pakistan Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia Department of Information Technology and Security College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9783031530814

Multiple users can jointly train a universal model using federated learning, a revolutionary AI approach, without having to reveal their personal data. With this strategy, anonymity is maintained while a secure learning environment is guaranteed. In contrast to the Workplace 31 dataset, this study presents a brand-new dataset made up of 23,326 photos obtained from eight different corporate sources and painstakingly organised into 31 classifications. It is noteworthy that this dataset is the first image classification dataset created specifically for federated learning. Along with the dataset, we also suggest the revolutionary federated learning algorithms Handled Cyclic and Handled Star. A cycle progression is established via Handled cycle, whereby a user gets weights from a previous user, adjusts them through localised training, and then transmits them to the following user. The essential job of image categorization within the context of computer vision is considerably improved by this novel approach, which highlights the development of federated learning. Traditional centralised deep learning model training requires centralised access to enormous datasets, which raises questions about data security and privacy. Federated learning addresses these issues by facilitating dispersed device collaboration for model training while restricting raw data exposure. This paper highlights the crucial role that federated learning has had in furthering AI research by giving an overview of its use in the context of image categorization. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image classification

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A Containerized IoT Simulation Environment for Network Warfare Training

A Containerized IoT Simulation Environment for Network Warfa...

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IEEE/ACM Int'l Conference on & Int'l Conference on cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Ruihao Chen Chengcheng Lyu Wenzhao Wang Hui Lin Qingxin Lin College of Computer and Cyber Security Fujian Normal University Fuzhou China Department of Computer Engineering Fuzhou University Zhicheng College Fuzhou China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Amidst the growing prevalence of Internet of Things (IoT) devices and the expanding risks to enterprise networks, there is a sustained increase in the demand for controlled penetration testing to delineate attack surfaces and uncover vulnerabilities within business operations. While the market offers a plethora of cybersecurity companies providing third-party penetration testing services, these services are often prohibitively expensive and fail to ensure data security. In response, we propose a lightweight environment emulation platform based on containerization, which abstractly simulates various IoT devices and environments from real-world settings, enabling the creation of controllable virtual environments that closely mimic actual business *** constructed virtual environment not only permits users to conduct attack simulations but also allows for the simultaneous inclusion of defensive strategies, thereby simulating a comprehensive set of attacker-defender scenarios. This significantly enhances the diversity and complexity of training exercises. This approach satisfies the requirements for data security and controllability, thereby strengthening the enterprise’s ability to both defend against and respond to cyber threats.

关键词： Training Social computing Data security Emulation Virtual environments cyber warfare Data models Internet of Things Penetration testing Business

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Decentralized Social Media using Blockchain and IPFS

Decentralized Social Media using Blockchain and IPFS

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Kachapilly Varghese Alfred Anthony T. Mathu Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Current social media platforms centralize user data, control content moderation, and employ opaque algorithms, raising concerns about privacy, censorship, and user control. By suggesting a decentralized social media platform that makes use of blockchain technology and the InterPlanetary File System, this study seeks to close this gap. The platform makes use of smart contracts for user authentication and trustless transactions, as well as the security and transparency of blockchain technology for data storage. Additionally, InterPlanetary File System offers decentralized storage for content, fostering censorship resistance. It features functionalities to add, remove, and view tweets posted to the blockchain by different wallet addresses. This approach empowers users with data ownership and control over their online interactions, contributing to a more equitable and inclusive digital society.

关键词： Resistance Pervasive computing Social networking (online) Smart contracts Authentication Censorship Trustless services

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A Moving Target DDoS Defense Approach in Consortium Blockchain

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Gai, Keke Zhang, Guolei Jiang, Peng Zhu, Liehuang Choo, Kim-Kwang Raymond Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China University of Texas at San Antonio Department of Information Systems and Cyber Security Department of Electrical and Computer Engineering San AntonioTX78249 United States

Although consortium blockchain has an identification mechanism, the captured internal clients are potentially threatening internal blockchain nodes. Internal Distributed Denial-of-Service (DDoS) attacks threaten the specific nodes in consortium blockchain, e.g., the executor, consensus, and committer nodes. Typical attack methods may include SYN Flooding and ACK Flooding and deny normal transaction service by sending many invalid transactions and blocks. In this work, we have proposed an organization collaboration-based DDoS defense approach and a Deep Q-learning (DQN)-based Moving Target Defense (MTD) for changing attack surface of victims in consortium blockchain. On one hand, contracts are used to synchronize attack information obtained from organizations, e.g., bots' IP addresses and public keys. On the other hand, we have developed a DQN-based MTD defense mechanism for organizations to change the attack surface of victims in order to mitigate the malicious traffic, in the case of missing detections of bots. Our approach applies a multi-stage game to reflect interactions between attackers and defenders. The evaluation results have demonstrated that our approach could effectively mitigate DDoS attacks in consortium blockchain. © 2025 IEEE.

关键词： Denial-of-service attack

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Enhancement of security in IoT using Secure Hash Cryptography Algorithm with Double Encryption (SHCA-DE) 9

Enhancement of Security in IoT using Secure Hash Cryptograph...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Dynamic Visible Light Positioning Based on Enhanced Visual Target Tracking

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China Communications 2023年第10期20卷 276-291页

作者： Xiangyu Liu Jingyu Hao Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

In visible light positioning systems,some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy,real-time performance,and ***,there are still two problems:(1)When the captured LED disappears and the uncertain LED reappears,existing tracking algorithms may recognize the landmark in error;(2)The receiver is not always able to achieve positioning under various moving *** this paper,we propose an enhanced visual target tracking algorithm to solve the above ***,we design the lightweight recognition/demodulation mechanism,which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the ***,we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning,when the receiver is under various moving *** results show that our system can achieve high-precision dynamic positioning and improve the system’s comprehensive performance.

关键词： visible light positioning visual target tracking gaussian mixture model kalman filtering system performance

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