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Mitigating Security Challenges in Edge computing: Attacks, Defence Strategies, and Algorithms

Mitigating Security Challenges in Edge Computing: Attacks, D...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Venkata Durga Rao Matta V.V.R. Maheswara Rao Venkata Sai Jayasurya Battula Mahesh Babu Addagatla Computer Science and Engineering Department Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies.

关键词： Technological innovation Scalability Data processing Real-time systems Security Resource management Internet of Things Interoperability Next generation networking Edge computing

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Towards Reliable CIDS: Hybrid Reputation and Challenge Strategies for Combating Internal Attacks

Towards Reliable CIDS: Hybrid Reputation and Challenge Strat...

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Robotics, Control and Automation (ICRCA), international conference on

作者： Yuandong Min Jingjing Zhang Sicong Liu Jia Li Furong Yu Jianjun Chen Hao Zhang National Engineering Research Center for E-Learning Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Wuhan Maritime Communication Research Institute (WMCRI) Wuhan China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Collaborative Intrusion Detection Systems (CIDS) serve as crucial firewalls, maintaining system security through necessary information exchange and integration among nodes in distributed systems. While CIDS excel at defending against external network attacks, internal threats remain a significant challenge. To identify malicious internal nodes, researchers typically adopt trust management mechanisms. However, current methods often fail to effectively take into account nodes' global reputation and historical behavior. Recognizing the similarities between distributed networks and social networks, we propose a hybrid trust computation mechanism based on social network reputation theory and develop a high-performance node clustering model. Simulated experiments conducted on a network with 50 nodes demonstrate that our method achieves higher accuracy and recall rates in identifying malicious nodes under adverse conditions compared to traditional methods, thereby enhancing the resilience of distributed networks against internal attacks.

关键词： Social networking (online) Firewalls (computing) Computational modeling Intrusion detection Reliability theory Network security Trust management Robots Information exchange Resilience

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Method for Filtering DDoS Attack Traffic through Routing Path within SDN Domain

Method for Filtering DDoS Attack Traffic through Routing Pat...

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IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Meng Yue Mingzhi Yang Hanwen Xu Zhijun Wu College of Safety Science and Engineering Civil Aviation University of China Tianjin China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Attacks that aim to disrupt services, known as distributed Denial of Service (DDoS), exploit protocol vulnerabilities and utilize legitimate traffic to target systems, thereby threatening the normal operation of the network. This paper proposes a method for filtering DDoS attack traffic along routing paths within the Software Defined networking (SDN) domain. The method, based on the SDN framework, employs a consistency hashing algorithm to select nodes in the global routing platform, forming secure access paths. By utilizing the accuracy of edge node detection and inter-domain cooperation, the attack is effectively mitigated along the path close to its origin, which is referred to as path filtering. The paper also provides a design for the filtering framework and details the filtering steps.

关键词： Filtering Image edge detection Collaboration Routing Denial-of-service attack Vectors Consensus protocol IP networks Computer crime Software defined networking

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ColdStart Reduction in Serverless Using Function Discovery and Distribution

ColdStart Reduction in Serverless Using Function Discovery a...

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international conference on Intelligence in Next Generation Networks (ICIN)

作者： Sanjeet Raj Pandey Maria Mora Martinez Patrick Herbke Service-centric Networking Technische Universität Berlin Berlin Germany

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Serverless, Function-as-a-Service (FaaS) is a cloud model that enables on-demand function execution with minimal management overhead for users, as cloud providers fully manage infrastructure. However, serverless computing suffers from a crucial drawback known as cold start latency, where initializing function containers from an idle state introduces substantial delays. Improving cold-start latency for serverless functions is essential for latency-sensitive applications, such as those used in 5G and 6G network infrastructures. This paper introduces a distributed middleware solution that reduces cold start latency by enabling function sharing and discovery across multiple nodes. We evaluated our solution using the OpenFaaS open-source platform, conducting experiments on a simulated network with varied latency conditions. Our middleware achieved a 80% reduction in cold start latency compared to a baseline OpenFaaS deployment without our middleware, demonstrating significant improvements in response times. Our approach minimizes container startup delays and improves resource efficiency by dynamically routing function requests to already initialized instances on neighboring nodes. These results indicate that our solution is well suited to enhance serverless applicability in next-generation mobile network environments, making serverless architectures viable for 5G Core networks and prospective 6G deployments.

关键词： 6G mobile communication Technological innovation 5G mobile communication Serverless computing Containers Routing Delays Time factors Middleware Next generation networking

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A distributed AI System for Improving Single-Domain Network Intent Resolution

A Distributed AI System for Improving Single-Domain Network ...

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international conference on Intelligence in Next Generation Networks (ICIN)

作者： Pedro Martinez-Julia Ved P. Kafle Hitoshi Asaeda Network Architecture Laboratory Network Research Institute National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Resolving network intents to network service descriptions (NSDs) is a key process in intent-based networking (IBN). Current state-of-the-art systems for single-domain intent resolution operate independently. Agents usually resolve the network intents of their domains without interacting with other agents. However, some aspects of the input network intents will be better interpreted and understood by other agents. We propose to use a distributed multi-agent system to enhance single-domain network intent resolution. Each agent of our proposed system delivers artificial intelligence (AI) operations through a machine-interactive interface. Agents make use of this interface to request other agents the resolution of parts of the input intents. The responses are gathered and a resulting NSD is produced. We demonstrate that adding external intelligence to the operation highly improves the network intent resolution process, reaching 100% accurate resolutions, which is more than 40% improvement over current state-of-the-art solutions.

关键词： Technological innovation Cloud computing Accuracy Artificial intelligence Multi-agent systems

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Improving Cloud Energy Efficiency through Machine Learning Models

Improving Cloud Energy Efficiency through Machine Learning M...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Eugenio Cesario Paolo Lindia Federica Lobello Andrea Vinci Shabnam Zarin Santina Capalbo DICES Department University of Calabria Rende Italy DIMES Department University of Calabria Rende Italy CNRNational Research Council of Italy Institute for HPC and Networking (ICAR) Rende Italy Computer Science Department Monmouth University West Long Branch NJ USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The rapid growth of Cloud computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive machine learning models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. A preliminary experimental evaluation, conducted on synthetic data, leverages predictive modeling within a server simulation to assess CPU and energy usage, demonstrating the effectiveness of energy-aware VMs allocation in optimizing resource utilization and reducing energy consumption.

关键词： Energy consumption Data centers Cloud computing Machine learning Predictive models Virtual machines Energy efficiency Servers Resource management Optimization

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Dynamic Staleness Control for Asynchronous Federated Learning in Decentralized Topology 18th

Dynamic Staleness Control for Asynchronous Federated Learni...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ma, Qianpiao Liu, Jianchun Jia, Qingmin Zhou, Xiaomao Hu, Yujiao Xie, Renchao Purple Mountain Laboratories Jiangsu Nanjing China School of Computer Science and Technology University of Science and Technology of China Anhui Hefei China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031714665

Decentralized federated learning (DFL) has emerged as a promising paradigm for distributed machine learning over edge nodes (i.e., workers) without relying on a centralized parameter server. Most existing DFL researches rely on synchronous communication among workers. However, due to edge heterogeneity and dynamic network conditions, synchronous DFL mechanisms may suffer from inefficient model training and poor scalability. Meanwhile, the existing asynchronous DFL (ADFL) mechanisms present the challenge of stale models among workers, leading to diminished training quality, especially on Non-IID data. In this paper, we propose a novel staleness-aware ADFL (SA-ADFL) mechanism, aiming to realize a trade-off between model training efficiency and quality by dynamic staleness control. Specifically, we provide rigorous theoretical proof for SA-ADFL and formulate the worker scheduling problem to minimize total model training time under flexible long-term staleness constraints. Then we decompose the original round-coupled problem into a series of single-round sub-problems by leveraging the Lyapunov optimization, enabling efficient worker selection to minimize training time in each round while ensuring staleness queue stability. Experimental results demonstrate that our SA-ADFL accelerates model training by approximately 52.9% while maintaining equivalent model training accuracy compared with the state-of-the-art mechanisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Methods for Identifying Implicit Communities and Analyzing Social Networks Using AI

Methods for Identifying Implicit Communities and Analyzing S...

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international Russian Smart Industry conference (SmartIndustryCon)

作者： Maxim A. Komardin Roman M. Britvin Pavel A. Panilov Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article provides an overview of modern methods and algorithms for detecting hidden communities in social networks, a crucial task given the rapid growth of data volumes and the complexity of network structures. The analysis covers clustering approaches and structural characterization of social graphs, including label propagation algorithms, density-based methods, as well as modularity and distributed computing algorithms. The paper explores the identification of implicit connections among users formed by shared interests, social environments, or behavioral patterns. A comprehensive evaluation of approaches to modeling network structures and interpreting interpersonal connections is provided, leveraging text content analysis, social interaction, and influence metrics. Challenges in computational efficiency and algorithm convergence for large-scale data analysis are discussed, alongside proposed improvements for testing and validation methods. Experimental studies validate the developed algorithms on various social graph structures, including real-world and synthetic data, highlighting the influence of topological and behavioral factors on community detection quality. The paper also describes some methods and algorithms for analysing users and communities.

关键词： Measurement Analytical models Visualization Social networking (online) Semantics Clustering algorithms Vectors Recommender systems Testing Synthetic data

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ZTAAC : Zero Trust Adaptive Authorization with CP-ABE for Context-Aware Data Protection

ZTAAC : Zero Trust Adaptive Authorization with CP-ABE for Co...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Kasturi Routray Padmalochan Bera Indian Institute of Technology Bhubaneswar India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In modern dynamic and distributed computing environments, secure and adaptive access control for sensitive data is essential. Traditional access control models often fail to address the complexities of evolving and heterogeneous systems. To overcome these challenges, we propose a Zero Trust Adaptive Authorization Architecture (ZTAAC) that integrates the principles of Zero Trust, Never trust, always verify, along with the cryptographic flexibility of a Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme. ZTAAC introduces a central zero trust controller, which incorporates a policy decision point and a risk engine to facilitate real-time risk assessment and dynamic policy enforcement. The risk engine evaluates contextual parameters such as user location, device posture, and behaviour patterns to enable real-time adaptation of access policies through feedback loops that refine access decisions dynamically. Our enhanced CP-ABE framework embeds static and dynamic attributes-based access policies within encrypted data, ensuring that only authorized users with valid context can decrypt it. Dynamic attribute management facilitates the generation and integration of key components for contextual attributes and aligns access decisions with current security postures. Preliminary performance analysis confirms ZTAAC’s scalability and efficiency, establishing it as a robust and adaptive solution for securing data in next-generation cloud-assisted and distributed environments.

关键词： Authorization Feedback loop Scalability distributed databases Real-time systems Zero Trust Performance analysis Risk management Engines Next generation networking

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Co-training with Progressive Distribution Alignment and Uncertainty-Interactive Relabeling for Semi-Supervised Domain Adaptive Semantic Segmentation

Co-training with Progressive Distribution Alignment and Unce...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruiguo Yu Yida Wang Xuewei Li Xuzhou Fu Zijian Zhang Yuan Tian Jie Gao College of Intelligence and Computing Tianjin University China Tianjin Key Laboratory of Cognitive Computing and Application China Tianjin Key Laboratory of Advanced Networking China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come from two aspects: First, the labels are severely imbalanced and distributed across domains and classes, which may lead to features biased towards source domains and majority classes. Second, the naive threshold filtering pseudo-label methods limit the supervision of hard pixels. To address the above problems, we propose a novel co-training framework with progressive distribution alignment and uncertainty-interactive relabeling strategies. More concretely, a progressive distribution alignment strategy is proposed to match distribution across domains while providing additional supervision for tail class pixels. Additionally, an uncertainty-interactive relabeling strategy is proposed to retain more supervisory information for hard pixels and reduce the overall uncertainty of the pseudo-labels. Experiments on two widely-used benchmarks demonstrate the effectiveness of the proposed PDAUR, achieving state-of-the-art results.

关键词： Adaptation models Uncertainty Filtering Semantic segmentation Semantics Tail Signal processing Predictive models Benchmark testing Speech processing

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