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检索条件"任意字段=International Conference on Security in Computer Networks and Distributed Systems, SNDS 2012"

共 5348 条记录，以下是11-20 订阅

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DDoS Mitigation Dilemma Exposed: A Two-Wave Attack with Collateral Damage of Millions 19th

DDoS Mitigation Dilemma Exposed: A Two-Wave Attack with Coll...

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19th international conference on security and Privacy in Communication networks

作者： Shi, Lumin Li, Jun Sisodia, Devkishen Zhang, Mingwei Dainotti, Alberto Reiher, Peter Univ Oregon Eugene OR 97403 USA Georgia Inst Technol Atlanta GA USA UCLA Los Angeles CA USA

ISBN: (纸本)9783031649530;9783031649547

While mitigating link-flooding attacks on the Internet has become an essential task, little research has been done on how an attacker can further attack and abuse the mitigation solutions themselves. In this paper, we propose a two-wave attack with collateral damage of millions (or Carom), a new link-flooding attack that poses a mitigation dilemma for multiple simultaneously attacked networks, which must either endure the flooding attack or suffer unwanted side effects in mitigating the attack. Composed of practical components, the Carom attack aims to maximize the burden on attack mitigation systems and the collateral damage to defending networks, thereby wreaking havoc on large swaths of the Internet. After modeling real-world mitigation solutions, we evaluated the attack against the mitigation solutions with real-world datasets, showing the feasibility of the attack and quantifying the amount of damage it can inflict on today's Internet. We hope that this work can motivate the improvement of existing link-flooding mitigation solutions.

关键词： Link-flooding attack distributed denial-of-Service (DDoS) Collateral damage

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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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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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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： distributed cloud

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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 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： Edge computing

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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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Securing Digital Integrity: Proposed Comprehensive Framework for Deepfake Detection and Blockchain Validation 5th

Securing Digital Integrity: Proposed Comprehensive Framework...

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5th international conference on Computing, Communications, and Cyber security, IC4S 2023

作者： Jain, Anant Gaur, Adamya Gupta, Gauranshi Mishra, Shubhangi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819773701

This paper proposes a comprehensive solution to combat the growing threat of Deepfake technology, employing Convolutional Neural networks (CNNs) and Blockchain. CNNs analyze video frames for anomalies indicative of Deepfake manipulation, while Blockchain ensures content integrity through timestamping and IPFS hashes. Leveraging transfer learning with EfficientNet-B1 architecture and dropout layers to prevent overfitting, the CNN model attains a 98.53% accuracy on testing data. Smart contracts are utilized to store social media content and AI verification results. The methodology integrates private storage on social media platforms, AI-based CNN verification, Blockchain timestamping, smart contract verification, and continuous improvement. By synergizing AI and Blockchain technologies, our approach aims to bolster defenses against deceptive content proliferation, enhancing the reliability of online media ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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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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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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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)9783031861956

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