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Group authentication algorithm based on region grouping and aggregate signcryption 4

Group authentication algorithm based on region grouping and ...

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4th international conference on Network Communication and Information security, ICNCIS 2024

作者： Li, Yong Gu, Jian Qiu, Jinyi He, Wenbo Zuo, Jiashuo State Grid Shaoxing Power Supply Company Zhejiang Shaoxing312000 China Department of Electronic and Communication Engineering North China Electric Power University Hebei Baoding071003 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

With the rapid development of the power Internet of Things and the increasing number of IoT devices and application scenarios, new requirements are put forward for the reliability and efficiency of equipment access certification. Aiming at the problems of low efficiency and malicious attack of traditional authentication methods, this paper proposes a group authentication algorithm based on regional grouping and aggregate signcryption. Firstly, according to the characteristics of IoT devices, a distributed authentication scheme based on regional grouping is designed. Then each group relies on aggregation signcryption technology for group authentication of IoT devices. Finally, the calculation and communication cost of the algorithm are compared and analyzed. The results show that the proposed algorithm can improve the authentication efficiency. © 2025 SPIE.

关键词： Network security computer security Internet of things Local area networks Data transmission Information security Telecommunications Zinc Computing systems Fiber optic gyroscopes

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Exploring Artificial Intelligence’s Potential in Developing Advanced distributed Denial of Service Defense Strategies

Exploring Artificial Intelligence’s Potential in Developing...

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international conference on Computing and Machine Learning, CML 2024

作者： Alhindi, Ahmad Computer Science and Artificial Intelligence Department College of Computer and Information Systems Makkah Saudi Arabia

ISBN: (纸本)9789819778386

Facing the growing menace of distributed denial of service (DDoS) attacks, there’s a critical demand for enhanced detection mechanisms. This necessitates a shift from traditional methodologies to more advanced, dynamic detection techniques. The significance of advanced computational methods in pinpointing DDoS threats is increasingly recognized. This paper delves into the crucial role these techniques play in detecting DDoS activities, with a focus on the adaptability of network-based algorithms and the proficiency of advanced models like Convolutional Neural networks (CNNs), Recurrent Neural networks (RNNs), and autoencoders in identifying complex attack patterns that traditional methods may overlook. Our findings reveal that while advanced computational strategies offer considerable benefits over conventional methods in terms of detection accuracy and adaptability to evolving threats, they also present unique challenges, including higher computational demands and the need for continuous model training to keep pace with new attack vectors. The comparative analysis underscores the operational advantages and potential limitations of these approaches, providing a nuanced understanding of their application in real-world scenarios. This investigation aims to present a current view of advancements in digital security techniques and to serve as a basis for future research and the development of innovative, practical solutions. It suggests that enhancing the resilience of digital infrastructures against DDoS attacks will require not only technological innovation but also a comprehensive strategy that includes ongoing research, collaboration among stakeholders, and the adoption of adaptive, scalable detection systems capable of responding to the dynamic nature of cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Atlas: Towards Real-Time Verification in Large-Scale networks via a Native distributed Architecture 25

Atlas: Towards Real-Time Verification in Large-Scale Network...

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20th European conference on computer systems, EuroSys 2025, co-located 30th ACM international conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Ma, Mingxiao Zhang, Yuehan Wang, Jingyu He, Bo Zhao, Chenyang Qi, Qi Zhuang, Zirui Sun, Haifeng Guo, Lingqi Guo, Yuebin Zhang, Gong Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Pengcheng Laboratory China *** China Huawei Technologies China

ISBN: (纸本)9798400711961

Data plane verification (DPV) can be critical in ensuring the network operates correctly. To be useful in practice, they need to be: (1) fast so as to prevent significant packet loss or security violations;(2) scalable so as to accommodate today’s large-scale network architecture. Current DPV tools struggle to meet these requirements due to their centralized architecture. To be concrete, there is a bottleneck for a single-point server to perform real-time DPV tasks. Furthermore, a single-point server makes it hard to collect real-time data plane updates from every device in large-scale networks. This paper proposes Atlas, a native distributed data plane verification (DPV) solution, which systematically solves its scalability problem and improves its real-time performance. To achieve this, (1) Atlas designs a hierarchical distributed architecture for DPV in large-scale networks. (2) Atlas proposes a native approach to maximize the parallelism of the architecture. Both dataset-driven simulation experiments and deployments in the wild demonstrate that Atlas is capable of fast and scalable distributed verification. The advantages offered by Atlas continue to expand as network sizes increase. Compared to state-of-the-art solutions, Atlas is 2-7× faster while keeping each component lightweight. © 2025 Copyright held by the owner/author(s).

关键词： Packet loss

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Enhancing Electric Vehicle Charging systems with distributed Edge Intelligence 16

Enhancing Electric Vehicle Charging Systems with Distributed...

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16th international conference on Ambient systems, networks and Technologies networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Rahmani, Rahim Stojcevska, Biljana Department of Computer and Systems Sciences Stockholm University Sweden American University of Europe Skopje North Macedonia

The rapid proliferation of electric vehicles (EVs) and the corresponding expansion of charging systems, driven by global regulatory mandates, present significant challenges to the electric grid. These systems also generate massive volumes of heterogeneous, distributed data, necessitating advanced edge-cloud computing solutions. Deploying distributed Edge Intelligence (DEI) offers a transformative approach by contextualizing raw data, minimizing dependency on centralized resources, enhancing task performance through experience-based learning, predicting outcomes under uncertainties, optimizing data routing, and enabling self-organization of networked devices. Traditional centralized machine learning approaches are often unsuitable due to high communication costs, low reliability, legal constraints, and scalability limitations. In this context, Federated Learning (FL) emerges as a promising solution, enabling distributed machine learning models to be trained locally on decentralized data while preserving privacy and reducing communication overhead. This paper provides a comprehensive analysis of the key challenges associated with EV charging systems, including scalability, security, and privacy concerns. It also explores the integration of blockchain technology with FL at distributed edges to enhance system efficiency, reliability, and privacy. By addressing these challenges, the study outlines a framework for sustainable EV adoption and efficient charging infrastructure deployment in the context of evolving technological and regulatory landscapes. © 2025 The Author(s).

关键词： Blockchain Charging systems distributed Edge Intelligence Electric Vechicle smart contract

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Towards Smarter Maritime security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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16th international conference on Ambient systems, networks and Technologies networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Apriori Algorithm Big Data Cloud-Native Architectures Data Mining distributed Computing Maritime Serverless Computing

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security Vulnerability Detection and Defense of Smart Home systems Based on the Internet of Things 6th

Security Vulnerability Detection and Defense of Smart Home S...

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6th international conference on Cyber security Intelligence and Analytics, CSIA 2024

作者： Zhao, Zhenghui Chen, Miao Chongqing Medical and Pharmaceutical College Chongqing401331 China

ISBN: (纸本)9783031882869

With the widespread application of IoT (Internet of Things) technology in smart homes, the security protection of systems has received more attention. In response to the problems of incomplete vulnerability detection and outdated defense mechanisms in traditional methods, this article proposed an efficient and comprehensive vulnerability detection and defense framework. By periodically scanning all terminal devices in the smart home system through edge devices, known vulnerabilities in the system were identified. Through edge computing technology and AI (Artificial Intelligence) algorithm detection, lightweight distributed defense mechanisms were deployed between intelligent devices to ensure that when an attack occurred, it can quickly respond locally and reduce the system response time. The experimental results showed that all devices in the edge computing-based scheme had a high coverage rate of more than 90%. In terms of the average response time, the edge computing defense framework combined with AI algorithm was 7.2 s, far lower than the traditional defense methods. The experimental results prove that this research is efficient in improving vulnerability detection and defense response. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Response time (computer systems)

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distributed Self-fault Outlier Detection in Wireless Sensor Network Based on Machine Learning 6th

Distributed Self-fault Outlier Detection in Wireless Sensor ...

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Padhi, Ruchika Mudulli, Debendra Gouda, Bhabani Sankar C. V. Raman Global University Odisha Bhubaneswar India School of Computer Science NIST Institute of Science and Technology Odisha Brahmapur India

ISBN: (纸本)9789819780891

Due to a wide range of essential applications, resource limitations, and intermittent irregular responses from cluster sensor nodes, different cluster sensor node faults and failures of distributed wireless sensor networks have occurred in recent years. Due to limited calculation capabilities, limited storage space, resource constraints, and a poor wireless communication environment, relatively few studies have been done on distributed fault diagnosis and fault tolerance in wireless sensor networks, making these networks vulnerable to impending security issues and abnormal detection. This chapter presents a thorough investigation into data quality, with a focus on the features of sensor node information with respect to time and space similarity, as well as recent advances in the study of outlier analysis. In this study, we employ classification to identify outliers by predicting the values of faulty sensor nodes using information gathered from their fault-free neighbors. The proposed method, DSFOD, uses the KNN algorithm to diagnose the proximity of a distance and achieves promising results of greater than 0.99. To accomplish the goal of data outlier detection in WSN, the data are analyzed and detected using the DSFOD method. The simulation’s findings demonstrate the network’s efficiency and superior performance at that specific time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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A distributed Technique of Optimization Problems Solving Based on Efficient Workload Assignment 27th

A Distributed Technique of Optimization Problems Solving Ba...

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27th international conference on distributed computer and Communication networks, DCCN 2024

作者： Klimenko, Anna Institute of IT and Security Technologies Russian State University for Humanities Moscow Russia

ISBN: (纸本)9783031808524

Currently, the efficiency improvement of complex technical systems is an urgent scientific task, which in numerous cases is formalized via optimization problem and solved by some well-known optimization methods. Metaheuristics are used intensively in this field due to their possibility to generate acceptable solutions in restricted time periods. However, it is known that with the decrease of performing time the solution accuracy of metaheuristics degrades, as well as algorithm’s convergence depends on its exploitation/exploration features and preliminary set parameters. Parallel metaheuristics implementations are used to improve algorithms performance. In this paper a distributed technique of computationally-hard optimization problems solving based on efficient workload assignment is presented and described. The novelty of the technique proposed is that the additional procedure of metaheuristics instances forming and distribution is added, which creates metaheuristic instances of a various computational complexity and assigns them to the most suited computing resources to perform parallel independent runs within the operation time restriction. The workload generated by metaheuristics blocks is distributed efficiently by means of metaheuristics portfolio usage. The latter makes it possible to improve the metaheuristics instances distribution and computational complexities forming so as to put the largest metaheuristic block to the node with the highest performance with the iterative improvement of the distribution, considering the full set of computing resources constraints and criteria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Enhancing Intrusion Detection in Smart Grids: A Machine Learning-based Defense Against DDoS Attacks with Imbalance-Aware Optimization 3

Enhancing Intrusion Detection in Smart Grids: A Machine Lear...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Mahaveerakannan, R. Kumar, T. Rajesh Balamanigandan, R. Vasarao, Parnandi Srinu Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Swarnandhra College of Engineering and Technology Department of Computer Science and Engineering West Godavari District Andhra Pradesh Narsapur India

ISBN: (纸本)9798331509675

The presence of numerous bidirectional communication devices connecting customers to the grid makes smart grid networks particularly vulnerable to network attacks. As an instance of a network attack that could impact the smart grid, a distributed denial of service (DDoS) attack occurs when a compromised grid communiqué devices or nodes send a flood of false data or requests to the smart grid network. This can cause problems with smart meters, data servers, and the state estimator, which in turn affects the services that end users receive. The problem of protecting the network from distributed denial of service (DDoS) attacks is best addressed by methods that rely on machine learning. One major drawback of using machine learning-based approaches is that models need to be retrained every time new types of attacks appear. Despite the fact that DDoS attack detection has long relied on Intrusion Detection systems (IDS), previous research has been limited due to a number of factors. Actually, it is highly discouraged to interfere with the regular functioning of the smart grid. to suggest using reconstructive deep learning methods to identify DDoS attacks efficiently and with minimal downtime in order to tackle this difficulty. In order to identify typical attacks on a communication network, this research introduces a shallow neural network with just 110 artificial neurones triggered by ReLU. to suggest a better attack-sharing loss function to handle unbalanced learning, which will allow for the training of such tiny neural networks. A Lungs Performance-based Optimisation (LPO) was devised using the suggested model to hone the parameters. In smart grid settings, our suggested approach improves the scalability and dependability of IDS and provides useful insights for building strong IDS based on machine learning. This enables organisations to strengthen their defences against harmful cyber-attacks and proactively reduce security risks. © 2025 IEEE.

关键词： Network intrusion

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14th EAI international conference on Wireless and Satellite systems, WiSATS 2024

14th EAI International Conference on Wireless and Satellite ...

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14th EAI international conference on Wireless and Satellite systems, WiSATS 2024

ISBN: (纸本)9783031862021

The proceedings contain 62 papers. The special focus in this conference is on Wireless and Satellite systems. The topics include: A DQN-Based Routing Algorithm for Load Balancing in LEO Satellite networks;design of Satellite Network Simulation Platform Supporting distributed Controller;Direct to Cell VLEO SatCom System Provide Low E2E Latency in STIN;dynamic Beam Optimization and Interference Mitigation Methods for Multi-beam Satellite systems;federated Learning-Based Cross-layer security Design for Satellite networks;DRL Based Secure Optimization for RIS Aided SATINs with RSMA;Research on the Signal Detection in an Uplink Ground-to-Satellite UDC-FSO System with Optical Path Difference;a High Precision Satellite Beam Agility Control Method;design of an Electro-Optical Hybrid Switching Architecture for Satellite Internet;genetic Algorithm-Based Inter-Satellite Link Establishment and Routing Scheme for Satellite networks;Kolmogorov-Arnold networks Based Signal Detection for OTFS systems in LEO Satellite Communications;Joint Computation Offloading and Resource Allocation for Low-Earth Orbit Satellites MEC networks;key Technologies and Future Developments in the Design of Spaceborne Digital Transparent Processors;A Solution to the Problem of Retail Credit Risk Pricing Problem Based on the Machine Learning XGBoost Algorithm;a Cooperative Spectrum Sensing Method Based on Feature Extraction and Fusion Clustering;An Optimized Design of Golden Angle Modulation SCMA Codebook Based on Genetic Algorithms;Bidirectional Backscatter NOMA Schemefor Efficient CDRT systems;Joint Equalization and Multi-phase Tracking Based on MCC Criterion for Underwater Acoustic Communication;ISAC Beamforming in Connected Autonomous Vehicles;Model-Driven Deep Learning for MIMO Signal Detection;integrated Sensing and Communication Empowered Secure Computation Offloading in Integrated Satellite-Terrestrial networks;Rotator-Aided RIS Beam Stabilization Method in UAV Communications;A Massive MIMO Antenna

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