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检索条件"任意字段=13th International Conference on Distributed Computing and Networking, ICDCN 2012"

共 299 条记录，以下是1-10 订阅

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Intelligent Intrusion Detection in Software-Defined networking: A Comparative Study of SVM and ANN Models 18

Intelligent Intrusion Detection in Software-Defined Networki...

引用

18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： Boukraa, Lamiae Essahraui, Siham El Makkaoui, Khalid Ouahbi, Ibrahim Esbai, Redouane Univ Mohammed Premier Multidisciplinary Fac Nador MASI Lab Oujda Morocco Univ Mohammed Premier Multidisciplinary Fac Nador Oujda Morocco

Software-defined networking (SDN) has emerged as a promising approach for managing network infrastructure through a centralized controller. However, the dynamic nature of SDN makes it susceptible to security threats, including DoS and DDoS attacks. Intrusion detection systems (IDS) based on machine learning (ML) can efficiently detect and mitigate these attacks. this study compares two ML models, namely support vector machines (SVM) and artificial neural networks (ANN), for intelligent intrusion detection in SDN. To assess the performance of the ML models, we utilized the NSL-KDD dataset, with a specific emphasis on DDoS attacks, and compared their accuracy (Acc), precision, recall, and F1-score metrics. the implementation outcomes show that SVM is better than ANN regarding response time and Acc. (c) 2023 the Authors. Published by Elsevier B.V.

关键词： Artificial neural Networks (ANN) distributed denial-of-service (DDoS) Intrusion detection system (IDS) Software-defined networking (SDN) Support vector machines (SVM)

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Efficient Differentially Private Tensor Factorization in the Parallel and distributed computing Paradigm 21

Efficient Differentially Private Tensor Factorization in the...

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21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Feng Wang, Hao Guo, Ruixin Xue, Erkang Qu, Guangzhi China University of Geosciences School of Computer Science Wuhan China Oakland University Department of Computer Science and Engineering Rochester United States

ISBN: (纸本)9798350329223

Tensor factorization plays a fundamental role in multiple areas of AI research. Nevertheless, it encounters significant challenges related to privacy breaches and operational efficiency. In this study, we propose a novel approach that addresses both of these issues simultaneously by integrating differential privacy with parallel and distributed computing. To accommodate diverse scenarios, we introduce two models: DPTF-SVRG and ADMM-DPTF, each leveraging specific techniques. DPTF-SVRG is designed for single-GPU environments and utilizes a unique strategy to reduce gradient variance, enabling faster convergence compared to SGD. Moreover, it achieves parallelism on the GPU through a lock-free asynchronous approach. On the other hand, ADMM-DPTF utilizes distributed ADMM to parallelize DPTF-SVRG, enabling multi-GPU parallelism. Experimental results demonstrate that our algorithms outperform existing benchmarks while maintaining differential privacy. © 2023 IEEE.

关键词： Tensors

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Proceedings - 2023 IEEE international conference on Parallel and distributed Processing with Applications, Big Data and Cloud computing, Sustainable computing and Communications, Social computing and networking, ISPA/BDCloud/SocialCom/SustainCom 2023

Proceedings - 2023 IEEE International Conference on Parallel...

引用

ISBN: (纸本)9798350329223

the proceedings contain 153 papers. the topics discussed include: transaction data management optimization based on multi-partitioning in blockchain systems;semi-asynchronous federated learning optimized for NON-IID data communication based on tensor decomposition;HKTGNN: hierarchical knowledge transferable graph neural network-based supply chain risk assessment;DQR-TTS: semi-supervised text-to-speech synthesis with dynamic quantized representation;deep reinforcement learning-based network moving target defense in DPDK;iNUMAlloc: towards intelligent memory allocation for AI accelerators with NUMA;and predictive queue-based low latency congestion detection in data center networks.

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model 21

Resource-Limited Localized Adaptive Adversarial Training for...

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作者： Rajhi, Mohammed Pissinou, Niki Florida International University School of Computing and Information Sciences MiamiFL United States Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia

ISBN: (纸本)9798350329223

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. this study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. this adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats. © 2023 IEEE.

关键词： Computational efficiency

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ADSwitch: Adaptive CPU Scheduling for distributed Systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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作者： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. the experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 IEEE.

关键词： Recurrent neural networks

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Privacy-Preserving Federated Learning through Clustered Sampling on Fine-Tuning distributed non-iid Large Language Models 21

Privacy-Preserving Federated Learning through Clustered Samp...

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作者： Yun, Sheng Bhuiyan, Zakirul Alam Sadi, Md. Taufiq Al Hasib Su, Shen Fordham University Department of Computer and Information Sciences New York10458 United States North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China

ISBN: (纸本)9798350329223

Recently, Large Language Models (LLMs) have been a phenomenal trend in the Artificial intelligence field. However, training and fine-tuning can be challenging because of privacy concerns and limited computing resources. Federated Learning (FL) has emerged as a novel machine learning framework offering privacy protection. the challenges in applying FL to real-world applications include dealing with heterogeneous data, poor client updates, and client selection. this paper introduces Privacy-preserving Federated Learning through Clustered Sampling on LLMs (FCLM), a framework that clusters models by their distribution similarity. It helps the model group similar models to improve text data heterogeneity handling and privacy concerns in distributed machine-learning environments. the FCLM framework is implemented and evaluated using popular Language models and text data. the framework shows a robust performance over the heterogeneous text data, which can further extend to the use of more complex LLMs. © 2023 IEEE.

关键词： Privacy-preserving techniques

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. the main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. this encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Parallel and Batch Multiple Replica Auditing Protocol for Edge computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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作者： Li, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

the edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 IEEE.

关键词： Edge computing

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A Moving Target Defense Approach for the distributed Dynamic Network 21

A Moving Target Defense Approach for the Distributed Dynamic...

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作者： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

the distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed Network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural Network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 IEEE.

关键词： Network security

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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作者： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. the third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. the data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. the security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

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