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Toward Enhancing 6G Security and Resilience with Blockchain: A Case Study on Mitigating SUCI Replay Attacks

Toward Enhancing 6G Security and Resilience with Blockchain:...

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ieee international conference on Consumer Electronics (ICCE)

作者： Ilsun You Hoseok Kwon dept. Financial Information Security Kookmin University Seoul Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Security/resilience is one of the four overarching aspects of 6G requirements. To enhance resilience, one potential technology that can be integrated into the network is blockchain. By leveraging synchronized and distributed peers participating in the blockchain network, blockchain technology mitigates single points of failure and provides enhanced resilience. To demonstrate the potential application of blockchain in 6G networks, we analyzed the SUCI(Subscription Concealed Identifier) replay attack reported by the 3GPP(3rd Generation Partnership Project) and proposed a blockchain-based solution to address this issue. Using an open-source 5G project, we implemented and tested the feasibility of integrating blockchain into the network, showcasing its potential to enhance security and resilience in next-generation communication systems.

关键词： 6G mobile communication 5G mobile communication Smart contracts Network security Blockchains 3GPP Synchronization Next generation networking Consumer electronics Resilience

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive sensor Networks

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Karthik Jakranpally Nagabathula Ramesh Babu Doddavarapu Sreenu Babu K. Rakesh Amit Barve Atmaram Shelke Valiant IT Services4000 Stanwick ave Sacramento CA Department of Electronics and Communication Engineering J.B. Institute of Engineering & Technology Hyderabad India Department of Electronics and Communication Engineering Bharatratna Indira Gandhi College of Engineering Solapur India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University P.O.Limda Gujarat India Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks.

关键词： Cross layer design Graph convolutional networks Reinforcement learning Throughput Routing Topology Synchronization Reliability Optimization Radio spectrum management

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Ensemble Classifier-based Intrusion Detection System for WSNs: Optimizing Security Against Evolving Attacks

Ensemble Classifier-based Intrusion Detection System for WSN...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： M. Nirmal Kumar T. Vijayan B. Karthik Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper presents an efficient Intrusion Detection System specified to the requirements of WSNs, targeting blackholes, floods, and scheduling-related intrusion attacks. The increasing vulnerability of WSNs to evolving attack patterns and their constrained resources make traditional security solutions, such as cryptography and trust-based systems, inadequate. The most widely used security technologies employ cryptography and trust-based systems have built-in shortcomings at protecting WSNs against evolving attack patterns and constricted resources. The proposed IDS will circumvent these challenges because it uses ensemble classifiers, namely REP Tree, Random Forest, and JRip, that demonstrate significant performance in the heavily constrained environment and unbalanced datasets. The results show that the system outperforms traditional IDS methods, with reduced resource consumption and a low false positive rate. Using the WSN-DS benchmark dataset, the system managed an average accuracy of 98.3% and a precision of 97.1%. This system is optimal for use in actual WSN installations because of its better performance than traditional IDS methods and small consumption of the resources.

关键词： Measurement Integrated circuits Wireless sensor networks Accuracy Scalability Intrusion detection Real-time systems Cryptography Floods Random forests

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Optimized Low-Power Mixed-Signal sensor systems: Improved Cross-Domain Approach to Maintain Classification Accuracy

Optimized Low-Power Mixed-Signal Sensor Systems: Improved Cr...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： M. Kiran Kumar Muntather Musnmusawi Sarangam Kodati Suresh G R T. Suresh Department of Electronics and Communication Engineering Anurag University Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Information Technology CVR College of Engineering Hyderabad India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Optimizing the mixed-signal systems-on-Chips (SoCs) is a difficult task, particularly while including analog developing blocks and Machine Learning (ML)-based algorithms that make overall performance hard to predict. These designs for minimizing energy consumption increases the lifetime from harvested energy from the environment. In this manuscript, a method is developed for optimizing difficult mixed-signal sensor system that includes three phases. Initially, Pareto-optimal local performance of individual circuits is modelled, developing the macro-model of mixed-signal system performance involving local nonidealities and identifying optimum group of system-level parameters utilizing numerical optimization. The method is employed for Electrocardiogram (ECG) sensor system with integrated arrhythmia classification with minimizing system energy consumption and constraints on inference accuracy. Various numerical optimization algorithms are compared to evaluate its performance for minimizing task are Bayesian Optimization and Gradient-based optimization. The experimental results obtained optimization in reasonable execution time of 9.5 h while compared to existing algorithms on complex mixed-signal system.

关键词： Energy consumption Accuracy System performance Machine learning Electrocardiography sensor systems Inference algorithms Numerical models Integrated circuit modeling Optimization

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Optimization of Node Deployment in Underwater sensor Networks Using Maximum Coverage and Connectivity Self-Deployment Algorithm Based on Particle Swarm Optimization

Optimization of Node Deployment in Underwater Sensor Network...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： G. Renuka V. Gurumurthy Raed Hameed P. K. Chidambaram Sudhakar Chinnakathiyan Department of Electronics and Communication Engineering Anurag University Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan University Trichy India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, Underwater Wireless sensor Networks (UWSNs) monitor underwater environment such as aquatic life. Moreover, to enhance network efficiently, sensor nodes are strategically placed in a 3D underwater space. However, underwater networks failed to find the optimal depth for each sensor in a 3D underwater network and failed in maintaining the connectivity to the surface station. Further, this research proposes a Maximum Coverage and Connectivity self-deployment algorithm based on Particle Swarm Optimization namely (MCC-PSO) for maximum coverage of water surface. Initially, the system models such as Network Scenario and Coverage Model are designed to optimize node deployment in UWSNs by simulating realistic environments. Then the problem identified that the optimal depth for N sensors deployed on a water surface to maximize network coverage and ensure connectivity to the surface station. To address this, a MCC and Connectivity Scheme is designed, Further, the MCC-PSO enhanced for optimizing the node development and PSO optimizes moving particles in search space by dynamic adaptation to environmental factors, such as node distribution and communication range. From the results, proposed MCC-PSO achieves a 50% higher maximum coverage rate compared to the existing Growth Ring Style Uneven Node Depth-Adjustment Self-Deployment Optimization Algorithm (GRSUNDSOA).

关键词： Integrated circuits Wireless sensor networks Three-dimensional displays Heuristic algorithms Environmental factors Real-time systems Reliability Particle swarm optimization Optimization Monitoring

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Advanced Water Quality Assessment with IOT WSN and Multivariate Machine Learning Techniques

Advanced Water Quality Assessment with IOT WSN and Multivari...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Shofia Priyadharshini D G.P. Ramesh Department of Electronics and communication engineering St.Peter's Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Ensuring clean and safe water is a critical global challenge, necessitating advanced monitoring techniques for early detection of contamination. This research presents an loT-driven water quality monitoring framework that integrates wireless sensor networks (WSNs) with multivariate machine learning models to assess key water parameters in real time. The proposed system collects and transmits data to a cloud platform for analysis, employing predictive algorithms to estimate E. coli levels. Six machine learning models-Ridge Regression, Random Forest Regressor, Stochastic Gradient Boosting, Support Vector Machine, k-Nearest Neighbors, and AdaBoost-were evaluated for their predictive accuracy. Among these, AdaBoost demonstrated the highest reliability, achieving the lowest mean absolute error (MAE) of 14.37 counts per 100 mL, while Stochastic Gradient Boosting showed the least accuracy. The study further identifies an optimal set of water quality parameters (Set A) comprising pH, conductivity, chloride, turbidity, nitrates, and chlorophyll, which enhances predictive efficiency. These findings highlight the potential of AI-driven water monitoring systems as early-warning tools, providing a cost-effective approach to ensuring water safety and public health.

关键词： Support vector machines Wireless sensor networks Accuracy Stochastic processes Escherichia coli Water quality Boosting Water pollution Real-time systems Random forests

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Enhancing Coverage and Connectivity with Hybrid Harris Hawk and Arithmetic Optimization in Clustering and Routing

Enhancing Coverage and Connectivity with Hybrid Harris Hawk ...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Sreelakshmi Tadigotla Jayanthi K Murthy Department of Electronics and Communication Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Wireless sensor Networks (WSN) focus on designing energy efficient mechanisms by selecting an optimal Cluster Head (CH) and implementing secure routing protocols. The WSN aims to reduce energy consumption while dynamically maximizing network efficiency. However, secure routing and Cluster Head (CH) selection in dynamic networks is optimal but challenging in WSNs. To overcome this problem, a hybrid optimization-based algorithm, Harris Hawks Optimization with Arithmetic Optimization Algorithm HHAOA is proposed to choose the optimal CHs in a dynamic network. The proposed HHAOA is utilized for solving coverage and connectivity issues in the routing algorithm. The proposed HHAOA system minimizes energy consumption for superior network performance. Additionally, multi-hop transmission and distance-based fitness function are employed for efficient CH and route path selection to ensure that coverage and connectivity parameters are taken into consideration for model proficiency with minimal energy consumption. The fitness function is used to measure the distance between CH and Base Station (BS), node degree, location factor, energy and distance for optimal CH and routing path selection. The proposed HHAOA consumes 63 (J) energy for 100 nodes with a throughput of 98 (kbps) for each100 rounds.

关键词： Wireless sensor networks Energy consumption Base stations Heuristic algorithms Clustering algorithms Spread spectrum communication Routing Throughput Energy efficiency Optimization

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8th international conference on Autonomic computing, ICAC 2011 Co-located Workshops - Proceedings of the 3rd Workshop on Biologically Inspired Algorithms for distributed systems, BADS 2011: Foreword

8th International Conference on Autonomic Computing, ICAC 20...

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8th international conference on Autonomic computing, ICAC 2011 Co-located Workshops - Proceedings of the 3rd Workshop on Biologically Inspired Algorithms for distributed systems, BADS 2011 2011年 iii-iv页

作者： Folino, Gianluigi Mastroianni, Carlo Mostaghim, Sanaz Suzuki, Junichi Italy Germany University of Massachusetts Boston MA United States

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Design and Implementation of a Personalized Recommendation System Based on Deep Learning distributed Collaborative Filtering Algorithm on Social Media Platforms

Design and Implementation of a Personalized Recommendation S...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Junyu Chen Alexa Music Amazon Seattle WA United States

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Social media platforms mainly provide personalized recommendation services for users, and usually adopt recommendation methods related to user or project similarity based on the characteristics of different platforms. However, current recommendation techniques mostly rely on simple similarity calculations, which can significantly reduce accuracy when dealing with large amounts of data. To address this issue, this paper proposes a collaborative filtering recommendation method based on social media gene maps. Specifically, this article draws on the concept of genes in biology, collects social media user behavior data, extracts biological genes, social genes, and network gene fragments, calculates the core gene matching degree of each gene fragment, and uses entropy weight method to assign different weights to gene fragments, thereby constructing a complete social media gene map and optimizing the recommendation mechanism. The experimental results show that this method outperforms traditional recommendation algorithms in terms of recommendation performance. Subsequently, this article proposes to combine social media gene maps with project similarity to further improve recommendation algorithms. When measuring project similarity, a fusion of scoring similarity and semantic similarity was used, combined with user gene matching degree, to optimize the weighted Slope One algorithm and achieve more accurate recommendation services. The results indicate that the algorithm proposed in this paper has significantly improved in evaluation metrics such as MAE and MRSE.

关键词： Weight measurement Accuracy Social networking (online) Collaborative filtering Diversity reception Semantics Biology User experience Real-time systems Recommender systems

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： K. Tamilselvan T. Senthil B. Buvaneswari Santhiya S Mukesh Soni Department of Information Technology AVS Engineering College Salem India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies chittoor India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer science and Engineering Excel Engineering College Namakkal India Department of Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network.

关键词： Deep learning Wireless sensor networks Wireless networks Transfer learning Training data sensors Resource management Optimization Spectral analysis Radio spectrum management

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