2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)最新文献_第8页

Securing Serverless Workflows on the Cloud Edge Continuum 保护云边缘连续体上的无服务器工作流

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00032

Gabriele Morabito, Christian Sicari, Lorenzo Carnevale, A. Galletta, G. Modica, M. Villari

引用次数: 0

Cognitive Health Assessment of Decentralized Smart home Activities using Federated Learning 使用联邦学习的分散式智能家居活动认知健康评估

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00024

A. R. Javed, Chun-Wei Lin, Gautam Srivastava

{"title":"Cognitive Health Assessment of Decentralized Smart home Activities using Federated Learning","authors":"A. R. Javed, Chun-Wei Lin, Gautam Srivastava","doi":"10.1109/CCGridW59191.2023.00024","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00024","url":null,"abstract":"The Internet of Things (IoT) and smart homes provide privacy-preserving environments for the healthcare sector to manage the care of individuals with cognitive impairment or disability. These homes, equipped with various sensors, can assist in assessing cognitive health by collecting data on daily activities. As cognitive health deteriorates over time, it can often go unnoticed until it is too late. In the literature, various machine learning and deep learning techniques have been applied to assess daily tasks and differentiate between individuals with competent and impaired cognitive abilities. However, this may compromise the privacy of those living in smart homes. This paper presents a federated learning approach based on deep neural networks to address this concern. The deep neural network model is trained on a cognitive health dataset and implemented on two clients, with a server used to receive updates from both. The results are evaluated in two rounds to reduce overfitting. The experiment demonstrates the effectiveness of the proposed approach, achieving more than 99.2% accuracy while maintaining data privacy.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129325037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse-HeteroCL: From Sparse Tensor Algebra to Highly Customized Accelerators on FPGAs 稀疏-异ocl:从稀疏张量代数到fpga上的高度定制加速器

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00061

Jize Pang, Lei Gong, Chao Wang, Xuehai Zhou

{"title":"Sparse-HeteroCL: From Sparse Tensor Algebra to Highly Customized Accelerators on FPGAs","authors":"Jize Pang, Lei Gong, Chao Wang, Xuehai Zhou","doi":"10.1109/CCGridW59191.2023.00061","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00061","url":null,"abstract":"Hardware-oriented domain-specific languages and hardware autogeneration pipelines for computationally intensive applications have received widespread attention because they can reduce the complexity of custom accelerator design and enable efficient FPGA accelerator generation. However, the existing hardware autogeneration tools are intended only for general computing and lack support for sparse tensor computation. To solve this problem, we present an end-to-end compilation tool called Sparse-HeteroCL, which inherits the idea of decoupling algorithm specification and customization from HeteroCL and expands it in three aspects: data structure, program description and computation schedule. In a preliminary performance evaluation, the workload incurred when using this compilation tool to write sparse tensor accelerators was compared with the corresponding workloads based on HeteroCL and HDL. The results show that compared with these existing languages, the programming efficiency is increased by average factors of 5.94 and 386.7, respectively, using our compilation tool.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127327026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks 在支持6g的物联网网络中用于网络威胁搜索的生成人工智能

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-03-21 DOI: 10.1109/CCGridW59191.2023.00018

M. Ferrag, M. Debbah, M. Al-Hawawreh

{"title":"Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks","authors":"M. Ferrag, M. Debbah, M. Al-Hawawreh","doi":"10.1109/CCGridW59191.2023.00018","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00018","url":null,"abstract":"The next generation of cellular technology, 6G, is being developed to enable a wide range of new applications and services for the Internet of Things (IoT). One of 6G’s main advantages for IoT applications is its ability to support much higher data rates and bandwidth as well as to support ultralow latency. However, with this increased connectivity will come to an increased risk of cyber threats, as attackers will be able to exploit the large network of connected devices. Generative Artificial Intelligence (AI) can be used to detect and prevent cyber attacks by continuously learning and adapting to new threats and vulnerabilities. In this paper, we discuss the use of generative AI for cyber threat-hunting (CTH) in 6G-enabled IoT networks. Then, we propose a new generative adversarial network (GAN) and Transformer-based model for CTH in 6Genabled IoT Networks. The experimental analysis results with a new cyber security dataset demonstrate that the Transformer-based security model for CTH can detect IoT attacks with a high overall accuracy of 95%. We examine the challenges and opportunities and conclude by highlighting the potential of generative AI in enhancing the security of 6G-enabled IoT networks and call for further research to be conducted in this area.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123904862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection DOC-NAD:用于网络异常检测的混合深度单类分类器

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2022-12-15 DOI: 10.1109/CCGridW59191.2023.00016

Mohanad Sarhan, Gayan K. Kulatilleke, Wai Weng Lo, S. Layeghy, Marius Portmann

{"title":"DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection","authors":"Mohanad Sarhan, Gayan K. Kulatilleke, Wai Weng Lo, S. Layeghy, Marius Portmann","doi":"10.1109/CCGridW59191.2023.00016","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00016","url":null,"abstract":"Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malicious) network data classes during the training phase. However, attack data samples are often challenging to collect in most organisations due to security controls preventing the penetration of known malicious traffic to their networks. Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples. The novel one-class classification architecture consists of a histogram-based deep feed-forward classifier to extract useful network data features and use efficient outlier detection. The DOC classifier has been extensively evaluated using two benchmark NIDS datasets. The results demonstrate its superiority over current state-of-the-art one-class classifiers in terms of detection and false positive rates.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114473764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Isolation-aware Online Virtual Network Embedding via Deep Reinforcement Learning 基于深度强化学习的隔离感知在线虚拟网络嵌入

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2022-11-25 DOI: 10.1109/CCGridW59191.2023.00028

Ali Gohar, Chunming Rong, Sanghwan Lee

{"title":"An Isolation-aware Online Virtual Network Embedding via Deep Reinforcement Learning","authors":"Ali Gohar, Chunming Rong, Sanghwan Lee","doi":"10.1109/CCGridW59191.2023.00028","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00028","url":null,"abstract":"Virtualization technologies are the foundation of modern ICT infrastructure, enabling service providers to create dedicated virtual networks (VNs) that can support a wide range of smart city applications. These VNs continuously generate massive amounts of data, necessitating stringent reliability and security requirements. In virtualized network environments, however, multiple VNs may coexist on the same physical infrastructure and, if not properly isolated, may interfere with or provide unauthorized access to one another. The former causes performance degradation, while the latter compromises the security of VNs. Service assurance for infrastructure providers becomes significantly more complicated when a specific VN violates the isolation requirement. In an effort to address the isolation issue, this paper proposes isolation during virtual network embedding (VNE), the procedure of allocating VNs onto physical infrastructure. We define a simple abstracted concept of isolation levels to capture the variations in isolation requirements and then formulate isolation-aware VNE as an optimization problem with resource and isolation constraints. A deep reinforcement learning (DRL)-based VNE algorithm ISO-DRL VNE, is proposed that considers resource and isolation constraints and is compared to the existing three state-of-the-art algorithms: NodeRank, Global Resource Capacity (GRC), and Mote-Carlo Tree Search (MCTS). Evaluation results show that the ISO-DRL VNE algorithm outperforms others in acceptance ratio, long-term average revenue, and long-term average revenue-to-cost ratio by 6%, 13%, and 15%.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117125806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0