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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： B Rajanarayan Prusty Kishore Bingi Debashisha Jena Manjulata Badi Mohan Krishna S. Department of Electrical and Electronics Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Electrical and Electronics Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of Electrical and Electronics Engineering National Institute of Technology Karnataka Surathkal India

This paper discusses the importance of adopting a spatiotemporal model-based scenario generation technique by approximating a multivariate continuous stochastic process in a discrete form. The computational steps for such a model development are comprehensibly discussed via a case study using PV generation data collected from the USA. The model accuracy comparison through a detailed discussion of obtained results is likely to help novice researchers to choose the best model amongst the available options and tactically decide to improvise the modeling framework further. This systematically prepared paper is expected to help power engineers with enough confidence to execute operational planning decisions realistically.

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Data Analytics Using Machine Learning For Cyber Security Safeguards Individual Privacy

Data Analytics Using Machine Learning For Cyber Security Saf...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sruthy S Zina Ravindran AkhilKK Gouri M. S Mohammed Waheeduddin Hussain Jothikumar R Department of Computer Science and Engineering (Cybersecurity) St. Joseph’s College of Engineering and Technology Palai Kerala Department of Computer Science and Artificial Intelligence Engineering Baselios Mathews II College of Engineering Sasthamcotta Department of Computer Science and Design Vimal Jyothi Engineering College Chemperi Department of Computer Science and Engineering Baselios Mathews II College of Engineering Lake View Kerala India Department of Information Technology Shadan College of Engineering and Technology Peerancheru Hyderabad India Department of Computer Science and Engineering Shadan College of Engineering and Technology Peerancheru Hyderabad India

The merging of data analytics and machine learning offers tremendous potential for boosting cyber security and protecting individual privacy in an age when personal data and cyber dangers are abundant. The purpose of this study was to create, test, and publish a model that takes into account this junction. Accuracy, precision, recall, F1-Score, and area under the receiver operating characteristic curve (AUC-ROC) are only a few of the indicators that show how well the framework can identify cyber threats. Further, the use of privacy-preserving methods like differential privacy highlights the moral need to guard sensitive information. Readers are given a concrete knowledge of the framework's real-world effect via vivid depictions of its practical use based on hypothetical data sets. This study serves as a helpful resource for a varied audience, from consumers worried about data privacy to organisations looking to better their cyber security defenses and legislators aiming to safeguard citizens' data. It also provides a springboard for further study in areas such as privacy enhancement, more sophisticated machine learning models, and sector-specific framework development. The digital ecosystem is always changing, but this study provides a guide for overcoming obstacles and capitalizing on possibilities at the intersection of cyber security and personal privacy.

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When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study

arXiv

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arXiv 2024年

作者： Xiong, Yang Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Xiong, Zehui Liang, Ying-Chang Mao, Shiwen Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore Computer Science and Design Pillar University of Technology and Design Singapore Chengdu611731 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework’s application in networking. We begin by exploring RAG’s applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs’ structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG’s enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications. © 2024, CC BY.

关键词： Knowledge graph

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Blockchain-Enhanced Privacy preserving Security scheme for Wireless Body Area Networks 5

Blockchain-Enhanced Privacy preserving Security scheme for W...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sowmiya, L. Rajasekaran, Arun Sekar Suresh, A. Younas, Ammar Vimalnath, S. Nageswari, S. KPR Institute of Engineering and Technology M e VLSI Design Coimbatore India SR University Department of ECE Telangana Warangal506371 India Vellore Institute of Technology Department of Computational Intelligence Vellore India Tashkent State University of Law Department of Business Law Uzbekistan Department of ECE Karur Thalavapalayam639113 India CARE College of Engineering Department of Computer Science Tamilnadu Trichy India

ISBN: (纸本)9798350305449

Due to the emergence of telemedicine systems it is necessary to develop a system that gives accurate results and improves security in the network channel. Nowadays Wireless Body Area Network (WBAN) makes a vital role in providing a better telemedicine system. WBAN is a non-invasive mechanism and it can predict many unknown diseases accurately even when a medical practitioner cannot able to predict the diseases. In WBAN, the patient is connected to sensors such as Electro Encephalo Gram (EMG), Electro Cardio Gram (EEG), insulin pump and SPO2 meter. WBAN uses both implantable and wearable sensors. These sensors in the patient body collects the medical data of patient and sent it through the medical practitioner via network channel. Medical practitioner sees the stored medical data in medical server and writes prescription according to it. The problem statement is that the attacker may attack and take patient data or act like medical practitioner and spread false information inside the network channel. In order to prevent this, WBAN must have an enhanced system that prevent and guard against security attacks. In proposed system, initially the patient and medical practitioner register with their original identities in the cloud server. The cloud server issues public and private keys with an elliptic curve to patient and medical practitioner for anonymous authentication. After the process, cloud server collects the data of the patient and store it in a blockchain. Medical practitioner checks the data in the block chain and sends medical prescription after confirmation of authenticated patient. Using of blockchain with cloud server increases the resistant to security attacks and patient data will be all time in the blockchain so the medical practitioner can easily retrieve the patient data without any delay. The proposed work is strength to many security attacks and provided reduced computation cost for cloud based WBAN with blockchain. © 2023 IEEE.

关键词： Authentication

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An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

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computers, Materials & Continua 2021年第12期69卷 2967-2981页

作者： Arun Kumar Sivabalan Ambigapathy Mehedi Masud Emad Sami Jaha Sumit Chakravarty Kanchan Sengar Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Center for System Design Chennai Institute of TechnologyIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA

The article introduces Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC),known as hybrid waveform(NOMAFBMC),as two of the most deserving contenders for fifth-generation(5G)*** spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid *** compare the spectral efficiency of Orthogonal Frequency Division Multiplexing(OFDM),FBMC,NOMA,and *** is seen that the hybrid waveform outperforms the existing *** to Average Power Ratio(PAPR)is regarded as a significant issue in multicarrier *** combination of Selective Mapping-Partial Transmit Sequence(SLM-PTS)is an effective way to minimize large peak power *** SLM,PTS,and SLM-PTS procedures are applied to the NOMA-FBMC *** hybrid structure is applied to the existing ***,the correlated factors like Bit Error Rate(BER)and Computational Overhead(CO)are studied and computed for these *** outcome of the work reveals that the NOMA-FBMC waveform coupled with the SLM-PTS algorithm offers superior performance as compared to the prevailing systems.

关键词： 5G NOMA-FBMC SLM-PTS PAPR BER OFDM

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A Secure and Energy-Efficient Cross-Layer Framework for Internet of Things

A Secure and Energy-Efficient Cross-Layer Framework for Inte...

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International Conference on Information Networking

作者： Rashid Mustafa Nurul I Sarkar Mahsa Mohaaghegh Shahbaz Pervez Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Information Technology Whitecliffe College of Art and Design Auckland New Zealand

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

This study addresses the problem of maximizing the power consumption of IoT (Internet of Things) devices while protecting them against cyberattacks. A layered architecture can be useful for addressing the unique challenges of security and energy efficiency in IoT systems. To this end, we propose a secure and energy-efficient cross-layer framework for IoT applications. The proposed cross-layer framework is based on collaboration among physical (sensor), network, and application layers. We examine the need for a three-layered approach in achieving the balance between security and energy efficiency. The proposed approach aims to provide a major leap in the areas of security and energy efficiency in IoT deployments. The system performance is validated by test-bed measurements with a focus on resource-constrained IoT settings. Using Cooja simulator we assess application-layer vulnerabilities and energy efficiency. The findings reported in this paper provide some insights into the design and deployment of IoT systems using a cross-layer design approach.

关键词： Cross layer design Collaboration Zigbee computer architecture Energy efficiency User experience Internet of Things

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Robustness of Multilayer Networks: A Graph Energy Perspective

SSRN

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SSRN 2023年

作者： Kumar, Rajesh Kumari, Suchi Mishra, Anubhav College of Engineering and Design Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Ghani Khan Choudhary Institute of Engineering and Technology West Bengal Malda India

Real-world complex systems such as Social, Technological, Infrastructure, etc., are inter-connected and interdependent. These systems are vulnerable to attacks failing nodes/edges. Most of the recent research works involve the study of robustness by evaluating the size of the largest connected component. For a given network size, the value of the size of the largest connected component is $1$ for the complete network. However, in the event of the cascade failure (attack) of some fraction of edges, the network becomes sparse and still may be the size of the largest connected component is $1$. There may be an instance at which the network system is critical, and any small perturbation may disintegrate the network system. Thus, in the present work, the robustness of the multilayer network system is evaluated by computing the efficiency and Laplacian and Quantum energies of the system during the cascade failure. Simulation results reveal that during the cascade failure, efficiency and Laplacian energy keep on decreasing up to some initial instances, and the robustness index is $1$. However, there is a critical point at which the system starts disintegrating for the given values of efficiency and Laplacian energy. For the Quantum energy scenario, the system is stable with minimum energy. During the cascade failure, the system begins to become unstable showing the increase in the Quantum energy at some critical point, and the robustness index begins to decrease. © 2023, The Authors. All rights reserved.

关键词： Laplace transforms

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Minimization of energy consumption in MANET using optimized k-medoid PSO Clustering Model

Minimization of energy consumption in MANET using optimized ...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Jebakumar Immanuel D Aby K Thomas Prasath B Kanchana M Narasimharaj V Anandhasilambarasan D Department of Computer Science and Engineering SNS College of Engineering Coimbatore India Department of Electronics and Communication Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

The (MANET) mobile ad hoc network connect with one another across wireless networks without relying on a central hub for network operations. In a decentralised network, individual mobile nodes take on more and more responsibility for the overall architecture of the system. Given that the routing and data transmission between nodes are impacted by the network's changing topology. The nodes' role as routers in the constant routing and transmission of data bundles is made necessary by this inherent volatility. The problem is that the mobile nodes, which rely on batteries instead of a constant power source, are the biggest consumers of energy in the network because they have to constantly recharge their power. The proposed work takes into account the delinquent of energy consumption in MANET, and finds that the optimization of energy constraint completely dominates the problem space. To begin, the MANET's mobile nodes are clustered using the suggested K-medoid clustering procedure, which helps to lessen the high cost of data routing in extremely large and dense networks. Modification of discrete particle swarm optimization procedure is a metaheuristic algorithm used to bargain the best possible value for the k-medoid algorithm, which is then used to develop a low-power protocol for exchanging information. packet delivery ratio, and throughput are the metrics used to compare different protocols. The results of the simulation analysis show that using this approach reduces the time needed for the method to run and boosts the nodes' energy efficiency.

关键词： Measurement Energy consumption Clustering algorithms Quality of service Routing Throughput Routing protocols

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