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An Intelligent and Trust-Enabled Farming Systems With Blockchain and Digital Twins on Mobile Edge Computing 移动边缘计算上的区块链和数字双胞胎智能化、可信任的农业系统
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-20 DOI: 10.1002/nem.2299
Geetanjali Rathee, Hemraj Saini, Selvaraj Praveen Chakkravarthy, Rajagopal Maheswar

Advancement and flourishment in mobile edge computing (MEC) have motivated the farmers to deploy an efficient ecosystem in their farms. For further real-time monitoring and surveillance of the environment along with the deployment of intelligent farming, digital twin is considered as one of the emerging and most promising technologies. For proper optimization and utilization of physical systems, the physical components of the ecosystems are connected with the digital space. Further, the smart technologies and devices have convinced to address the expected level of requirements for accessing the rapid growth in farming associated with digital twins. However, with a large number of smart devices, huge amount of generated information from heterogeneous devices may increase the privacy and security concern by challenging the interrupting operations and management of services in smart farming. In addition, the growing risks associated with MEC by modifying the sensor readings and quality of service further affect the overall growth of intelligent farming. In order to resolve these challenges, this paper has proposed a secure surveillance architecture to detect deviations by incorporating digital twins in the ecosystem. Further, for real-time monitoring and preprocessing of information, we have integrated a four-dimensional trust mechanism along with blockchain. The four-dimensional trusted method recognizes the behavior of each communicating device during the transmission of information in the network. Further, blockchain strengthens the surveillance process of each device behavior by continuously monitoring their activities. The proposed mechanism is tested and verified against various abnormalities received from sensors by simulating false use cases in the ecosystem and compared against various security metrics over existing approaches. Furthermore, the proposed mechanism is validated against several security threats such as control command threat, coordinated cyber threats, accuracy, and decision-making and prediction of records over existing methods.

移动边缘计算(MEC)的进步和蓬勃发展促使农民在农场中部署高效的生态系统。为了进一步对环境进行实时监测和监控,同时部署智能农业,数字孪生被认为是最有前途的新兴技术之一。为了适当优化和利用物理系统,生态系统的物理组件与数字空间相连接。此外,智能技术和设备已确信能够满足与数字孪生相关的农业快速增长的预期要求。然而,随着智能设备的大量出现,来自异构设备的海量信息可能会增加隐私和安全问题,对智能农业服务的中断操作和管理构成挑战。此外,通过修改传感器读数和服务质量而与 MEC 相关的风险不断增加,进一步影响了智能农业的整体发展。为了解决这些挑战,本文提出了一种安全监控架构,通过将数字双胞胎纳入生态系统来检测偏差。此外,为了实现实时监控和信息预处理,我们将四维信任机制与区块链结合在一起。四维信任方法可识别网络信息传输过程中每个通信设备的行为。此外,区块链通过持续监控每个设备的活动,加强了对其行为的监控过程。通过模拟生态系统中的虚假用例,针对从传感器接收到的各种异常情况对所提出的机制进行了测试和验证,并与现有方法的各种安全指标进行了比较。此外,与现有方法相比,还针对控制指令威胁、协同网络威胁、准确性、决策和记录预测等几种安全威胁对所提出的机制进行了验证。
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引用次数: 0
ProKube: Proactive Kubernetes Orchestrator for Inference in Heterogeneous Edge Computing ProKube:用于异构边缘计算推理的主动式 Kubernetes 协调器
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-18 DOI: 10.1002/nem.2298
Babar Ali, Muhammed Golec, Sukhpal Singh Gill, Felix Cuadrado, Steve Uhlig

Deep neural network (DNN) and machine learning (ML) models/ inferences produce highly accurate results demanding enormous computational resources. The limited capacity of end-user smart gadgets drives companies to exploit computational resources in an edge-to-cloud continuum and host applications at user-facing locations with users requiring fast responses. Kubernetes hosted inferences with poor resource request estimation results in service level agreement (SLA) violation in terms of latency and below par performance with higher end-to-end (E2E) delays. Lifetime static resource provisioning either hurts user experience for under-resource provisioning or incurs cost with over-provisioning. Dynamic scaling offers to remedy delay by upscaling leading to additional cost whereas a simple migration to another location offering latency in SLA bounds can reduce delay and minimize cost. To address this cost and delay challenges for ML inferences in the inherent heterogeneous, resource-constrained, and distributed edge environment, we propose ProKube, which is a proactive container scaling and migration orchestrator to dynamically adjust the resources and container locations with a fair balance between cost and delay. ProKube is developed in conjunction with Google Kubernetes Engine (GKE) enabling cross-cluster migration and/ or dynamic scaling. It further supports the regular addition of freshly collected logs into scheduling decisions to handle unpredictable network behavior. Experiments conducted in heterogeneous edge settings show the efficacy of ProKube to its counterparts cost greedy (CG), latency greedy (LG), and GeKube (GK). ProKube offers 68%, 7%, and 64% SLA violation reduction to CG, LG, and GK, respectively, and it improves cost by 4.77 cores to LG and offers more cost of 3.94 to CG and GK.

深度神经网络(DNN)和机器学习(ML)模型/推断会产生高度精确的结果,需要大量的计算资源。终端用户智能小工具的容量有限,这促使公司在从边缘到云的连续过程中开发计算资源,并在面向用户的位置托管应用程序,以满足用户对快速响应的要求。Kubernetes 托管推论的资源请求估算能力较差,导致服务水平协议(SLA)遭到违反,表现为延迟和低于标准的性能,端到端(E2E)延迟较高。终身静态资源配置要么会因资源配置不足而损害用户体验,要么会因资源配置过多而产生成本。动态扩展可通过上调规模来弥补延迟,但这会导致额外的成本,而简单地迁移到另一个位置,在服务水平协议(SLA)范围内提供延迟,则可减少延迟并最大限度地降低成本。为了解决在固有的异构、资源受限和分布式边缘环境中进行 ML 推断所面临的成本和延迟挑战,我们提出了 ProKube,它是一种主动式容器扩展和迁移协调器,可动态调整资源和容器位置,在成本和延迟之间取得合理平衡。ProKube 是与谷歌 Kubernetes 引擎(GKE)联合开发的,可实现跨集群迁移和/或动态扩展。它还支持在调度决策中定期添加最新收集的日志,以处理不可预测的网络行为。在异构边缘设置中进行的实验表明,ProKube 的功效优于其同类产品成本贪婪(CG)、延迟贪婪(LG)和 GeKube(GK)。ProKube 比 CG、LG 和 GK 分别减少了 68%、7% 和 64% 的 SLA 违反率,比 LG 提高了 4.77 个内核的成本,比 CG 和 GK 提高了 3.94 个内核的成本。
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引用次数: 0
An IoT Intrusion Detection Approach Based on Salp Swarm and Artificial Neural Network 基于 Salp Swarm 和人工神经网络的物联网入侵检测方法
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-18 DOI: 10.1002/nem.2296
Omar A. Alzubi, Jafar A. Alzubi, Issa Qiqieh, Ala' M. Al-Zoubi

The Internet of Things has emerged as a significant and influential technology in modern times. IoT presents solutions to reduce the need for human intervention and emphasizes task automation. According to a Cisco report, there were over 14.7 billion IoT devices in 2023. However, as the number of devices and users utilizing this technology grows, so does the potential for security breaches and intrusions. For instance, insecure IoT devices, such as smart home appliances or industrial sensors, can be vulnerable to hacking attempts. Hackers might exploit these vulnerabilities to gain unauthorized access to sensitive data or even control the devices remotely. To address and prevent this issue, this work proposes integrating intrusion detection systems (IDSs) with an artificial neural network (ANN) and a salp swarm algorithm (SSA) to enhance intrusion detection in an IoT environment. The SSA functions as an optimization algorithm that selects optimal networks for the multilayer perceptron (MLP). The proposed approach has been evaluated using three novel benchmarks: Edge-IIoTset, WUSTL-IIOT-2021, and IoTID20. Additionally, various experiments have been conducted to assess the effectiveness of the proposed approach. Additionally, a comparison is made between the proposed approach and several approaches from the literature, particularly SVM combined with various metaheuristic algorithms. Then, identify the most crucial features for each dataset to improve detection performance. The SSA-MLP outperforms the other algorithms with 88.241%, 93.610%, and 97.698% for Edge-IIoTset, IoTID20, and WUSTL, respectively.

物联网已成为当代一项重要而有影响力的技术。物联网提出了减少人工干预需求的解决方案,并强调任务自动化。根据思科的一份报告,到 2023 年,物联网设备将超过 147 亿台。然而,随着使用这项技术的设备和用户数量的增加,安全漏洞和入侵的可能性也在增加。例如,智能家电或工业传感器等不安全的物联网设备很容易受到黑客攻击。黑客可能会利用这些漏洞未经授权访问敏感数据,甚至远程控制设备。为解决和防止这一问题,本研究提出将入侵检测系统(IDS)与人工神经网络(ANN)和沙蜂算法(SSA)相结合,以加强物联网环境中的入侵检测。SSA 作为一种优化算法,可为多层感知器(MLP)选择最佳网络。已使用三种新基准对所提出的方法进行了评估:Edge-IIoTset、WUSTL-IIOT-2021 和 IoTID20。此外,还进行了各种实验来评估所提出方法的有效性。此外,还对提出的方法和文献中的几种方法进行了比较,特别是 SVM 与各种元启发式算法的结合。然后,确定每个数据集最关键的特征,以提高检测性能。在 Edge-IIoTset、IoTID20 和 WUSTL 数据集上,SSA-MLP 的检测率分别为 88.241%、93.610% 和 97.698%,优于其他算法。
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引用次数: 0
Intrusion Detection for Blockchain-Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model 使用高斯混杂-完全卷积变异自动编码器模型对基于区块链的物联网进行入侵检测
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-18 DOI: 10.1002/nem.2295
C. U. Om Kumar, Suguna Marappan, Bhavadharini Murugeshan, P. Mercy Rajaselvi Beaulah

The Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain-based deep learning model for IDS. Initially, the input data are preprocessed using min-max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM-FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM-FCVAE model is analyzed by evaluating several metrics. The proposed GM-FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW-NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM-FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.

物联网(IoT)是一种不断发展的模式,它极大地改变了传统的生活方式,使之成为一种智能生活方式。最近,物联网设备因其在医疗保健、智能家居设备、智能工业、智能城市等各个领域的广泛应用而备受关注。然而,在物联网环境中,安全仍然是一个具有挑战性的问题。由于物联网设备各不相同,因此很难检测到物联网中存在的各种攻击。现有的各种研究旨在提供可靠的入侵检测系统(IDS)技术。但是,由于存在一些安全问题,它们未能奏效。因此,本研究提出了一种基于区块链的 IDS 深度学习模型。首先,使用最小-最大归一化对输入数据进行预处理,将原始输入数据转换为更高质量的数据。为了检测所提供数据集中的攻击,该研究引入了高斯混合-完全卷积变异自动编码器(GM-FCVAE)模型。该模型用 Python 实现,并通过评估多个指标分析了所提出的 GM-FCVAE 模型的性能。所提出的 GM-FCVAE 模型在三个数据集上进行了测试,在 UNSW-NB15、CICIDS 2019 和 N_BaIoT 数据集上的准确率分别达到了 99.18%、98.81% 和 98.4%。对比结果表明,所提出的 GM-FCVAE 模型比其他深度学习技术获得了更高的结果。优异的表现表明,所提出的研究在识别安全攻击方面卓有成效。
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引用次数: 0
An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks 基于智能强化学习的移动边缘网络威胁检测方法
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-12 DOI: 10.1002/nem.2294
Muhammad Yousaf Saeed, Jingsha He, Nafei Zhu, Muhammad Farhan, Soumyabrata Dev, Thippa Reddy Gadekallu, Ahmad Almadhor

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration.

传统的移动边缘网络威胁检测技术在适应不断变化的威胁方面能力有限。我们提出了一种基于智能强化学习(RL)的方法,用于移动边缘网络中的实时威胁检测。我们的方法能让代理根据环境反馈不断学习和调整其威胁检测能力。通过实验,我们证明我们的技术在动态边缘网络环境中检测威胁方面优于传统方法。我们基于 RL 的方法的智能性和适应性使其非常适合于保护具有严格延迟和可靠性要求的关键任务边缘应用。我们对多接入边缘计算中的威胁模型进行了分析,并强调了设备上学习在实现异构边缘节点分布式威胁情报中的作用。我们的技术具有潜力,能显著提高下一代移动边缘网络的威胁可见性和弹性。未来的工作包括优化我们方法的采样效率,并整合可解释的威胁检测模型,以实现值得信赖的人机协作。
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引用次数: 0
Blockchain-Enabled Decentralized Healthcare Data Exchange: Leveraging Novel Encryption Scheme, Smart Contracts, and Ring Signatures for Enhanced Data Security and Patient Privacy 区块链去中心化医疗数据交换:利用新型加密方案、智能合约和环形签名增强数据安全性和患者隐私保护
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-08 DOI: 10.1002/nem.2289
S. Vidhya, P. M. Siva Raja, R. P. Sumithra

The healthcare industry has undergone a digital transformation in recent years, with the adoption of electronic health records (EHRs) becoming increasingly prevalent. While this digitization offers various advantages, concerns regarding the security and privacy of sensitive medical data have also intensified. Data breaches and cyber-attacks targeting healthcare organizations have underscored the need for robust solutions to protect patient data. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature, which ensures secure and transparent data recording. This paper proposes a novel approach that combines blockchain with advanced encryption scheme and privacy protection technique to establish a secure and privacy protected medical data sharing environment. The proposed system consists of three phases such as initialization phase, data processing phase, and authentication phase. The hybrid Feistal-Shannon homomorphic encryption algorithm (HFSHE) is proposed to encrypt the medical data to ensure data confidentiality, integrity, and availability. Ring signature is integrated to the system to provide additional anonymity and protect the identities of the participants involved in data transactions. In addition, the smart contract developed performs authentication checks on users, generates a time seal, and verifies the ring signature. Through this enhancement, the system becomes more resilient to both external and internal threats, enhancing overall security as well as privacy. A comprehensive security analysis is conducted to compare the proposed method's performance against existing techniques. The results demonstrate the effectiveness of the proposed approach in safeguarding sensitive medical information within the blockchain ecosystem.

近年来,医疗保健行业经历了一场数字化变革,电子病历(EHR)的应用越来越普遍。虽然数字化带来了各种优势,但人们对敏感医疗数据的安全性和隐私性的担忧也在加剧。针对医疗机构的数据泄露和网络攻击凸显了保护患者数据的强大解决方案的必要性。区块链技术因其去中心化和不可更改的特性,确保了数据记录的安全和透明,已成为一种前景广阔的解决方案。本文提出了一种将区块链与先进的加密方案和隐私保护技术相结合的新方法,以建立一个安全和隐私保护的医疗数据共享环境。所提出的系统包括三个阶段,如初始化阶段、数据处理阶段和身份验证阶段。提出了混合费斯特尔-香农同态加密算法(HFSHE)来加密医疗数据,以确保数据的保密性、完整性和可用性。系统还集成了环形签名,以提供额外的匿名性并保护数据交易参与者的身份。此外,开发的智能合约会对用户进行身份验证检查、生成时间印章并验证环形签名。通过这一改进,系统可以更好地抵御外部和内部威胁,提高整体安全性和隐私性。我们进行了全面的安全分析,以比较建议方法与现有技术的性能。结果表明,所提出的方法能有效保护区块链生态系统中的敏感医疗信息。
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引用次数: 0
DELTA: A Modular, Transparent, and Efficient Synchronization of DLTs and Databases DELTA:模块化、透明、高效的数字签名技术与数据库同步技术
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-05 DOI: 10.1002/nem.2293
F. Javier Fernández-Bravo Peñuela, Jordi Arjona Aroca, Francesc D. Muñoz-Escoí, Yuriy Yatsyk Gavrylyak, Ismael Illán García, José M. Bernabéu-Aubán

Besides cryptocurrencies, DLTs may be also exploited in enterprise systems operated by a consortium of organizations. Their interaction takes usually place on a permissioned blockchain network that holds a set of data to be queried frequently. In this scope, the main problem of DLTs is their unsuitability for a fast service of complex queries on those data. In order to solve this issue, many proposals dump the ledger contents onto databases that, because of their own goals and design, are already optimized for the execution of those queries. Unfortunately, many of those proposals assume that the data to be queried consist in only a block or (cryptocurrency-related) transaction history. However, those organization consortiums commonly store other structured business-related information in the DLT, and there is an evident lack of support for querying that other kind of structured data. To remedy those problems, DELTA synchronizes, with minimal overhead, the DLT state into a database, providing (1) a modular architecture with event-based handling of DLT updates that supports different DLTs and databases, (2) a transparent management, since DLT end users do not need to learn or use any new API in order to handle that synchronization (i.e., those users still rely on the original interface provided by their chosen DLT), (3) the efficient execution of complex queries on those structured data. Thus, DELTA reduces query times up to five orders of magnitude, depending on the DLT and the database, compared to queries directed to the ledger nodes.

除了加密货币,DLT 还可用于由组织联盟运营的企业系统。它们之间的交互通常发生在经过许可的区块链网络上,该网络拥有一组需要经常查询的数据。在这种情况下,DLT 的主要问题是不适合快速提供对这些数据的复杂查询服务。为了解决这个问题,许多建议将分类账内容转储到数据库中,而数据库由于其自身的目标和设计,已经为执行这些查询进行了优化。遗憾的是,其中许多建议都假定要查询的数据只包括区块或(加密货币相关的)交易历史。然而,这些组织联盟通常会在 DLT 中存储其他结构化的业务相关信息,而且显然缺乏对其他类型结构化数据的查询支持。为了解决这些问题,DELTA 以最小的开销将 DLT 状态同步到数据库中,提供(1)模块化架构,基于事件处理 DLT 更新,支持不同的 DLT 和数据库;(2)透明管理,因为 DLT 终端用户不需要学习或使用任何新的应用程序接口来处理同步(即,这些用户仍然依赖其所选 DLT 提供的原始接口);(3)高效执行对这些结构化数据的复杂查询。因此,与针对分类账节点的查询相比,DELTA 可将查询时间缩短达五个数量级,具体取决于 DLT 和数据库。
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引用次数: 0
Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions 元网时代大型语言模型的隐私保护:研究前沿、分类比较和未来方向
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-07-29 DOI: 10.1002/nem.2292
Dabin Huang, Mengyu Ge, Kunlan Xiang, Xiaolei Zhang, Haomiao Yang

Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration.

大型语言模型(LLM)拥有数十亿到数万亿个参数,在自然语言处理、机器翻译、对话系统和文本摘要等方面表现出色。这些功能在元宇宙中越来越重要,因为它们可以增强虚拟交互和环境。然而,它们的广泛应用,尤其是在元宇宙的沉浸式平台中的应用,引发了严重的隐私问题。本文分析了 LLM 中现有的隐私问题,这些问题对传统应用和元宇宙应用都至关重要,并研究了这些模型从培训到用户部署的整个生命周期中的保护技术。我们深入研究了密码学、嵌入层编码、差分隐私及其变体和对抗网络,并强调了它们在元宇宙背景下的相关性。具体来说,我们探讨了同态加密和安全多方计算等技术,这些技术对元数据安全至关重要。我们对高斯差分隐私、仁义差分隐私、埃奇沃思会计以及对抗样本和损失函数的生成进行了讨论,强调了它们在元宇宙的动态和交互环境中的重要性。最后,本文讨论了元宇宙内外 LLM 安全的研究现状和未来挑战,强调了亟待解决的问题和潜在的探索领域。
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引用次数: 0
No Worker Left (Too Far) Behind: Dynamic Hybrid Synchronization for In-Network ML Aggregation 没有工人落在后面(太远):网络内 ML 聚合的动态混合同步
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-07-24 DOI: 10.1002/nem.2290
Diego Cardoso Nunes, Bruno Loureiro Coelho, Ricardo Parizotto, Alberto Egon Schaeffer-Filho

Achieving high-performance aggregation is essential to scaling data-parallel distributed machine learning (ML) training. Recent research in in-network computing has shown that offloading the aggregation to the network data plane can accelerate the aggregation process compared to traditional server-only approaches, reducing the propagation delay and consequently speeding up distributed training. However, the existing literature on in-network aggregation does not provide ways to deal with slower workers (called stragglers). The presence of stragglers can negatively impact distributed training, increasing the time it takes to complete. In this paper, we present Serene, an in-network aggregation system capable of circumventing the effects of stragglers. Serene coordinates the ML workers to cooperate with a programmable switch using a hybrid synchronization approach where approaches can be changed dynamically. The synchronization can change dynamically through a control plane API that translates high-level code into switch rules. Serene switch employs an efficient data structure for managing synchronization and a hot-swapping mechanism to consistently change from one synchronization strategy to another. We implemented and evaluated a prototype using BMv2 and a Proof-of-Concept in a Tofino ASIC. We ran experiments with realistic ML workloads, including a neural network trained for image classification. Our results show that Serene can speed up training by up to 40% in emulation scenarios by reducing drastically the cumulative waiting time compared to a synchronous baseline.

实现高性能聚合对于扩展数据并行分布式机器学习(ML)训练至关重要。最近的网络内计算研究表明,与传统的纯服务器方法相比,将聚合卸载到网络数据平面可以加速聚合过程,减少传播延迟,从而加快分布式训练。然而,关于网络内聚合的现有文献并没有提供处理速度较慢的工作者(称为 "游离者")的方法。散兵的存在会对分布式训练产生负面影响,增加训练完成所需的时间。在本文中,我们介绍了 Serene,一种能够规避散兵游勇影响的网内聚合系统。Serene 使用一种混合同步方法协调 ML 工作者与可编程交换机合作,这种方法可以动态改变。同步可通过将高级代码转换为交换规则的控制平面应用程序接口(API)动态更改。Serene switch 采用了一种高效的数据结构来管理同步,并采用了一种热插拔机制,可持续地从一种同步策略切换到另一种同步策略。我们使用 BMv2 实现并评估了一个原型,并在 Tofino ASIC 中进行了概念验证。我们使用现实的 ML 工作负载进行了实验,包括为图像分类而训练的神经网络。结果表明,与同步基线相比,Serene 通过大幅减少累计等待时间,可将仿真场景中的训练速度提高 40%。
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引用次数: 0
A Generalized Lightweight Intrusion Detection Model With Unified Feature Selection for Internet of Things Networks 针对物联网网络的统一特征选择的通用轻量级入侵检测模型
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-07-23 DOI: 10.1002/nem.2291
Renya Nath N, Hiran V. Nath

The applicability of the Internet of Things (IoT) cutting across different domains has resulted in newer “things” acquiring IP connectivity. These things, technically known as IoT devices, are vulnerable to diverse security threats. Consequently, there has been an exponential increase in IoT malware over the past 5 years, and securing IoT devices from such attacks is a pressing concern in the current era. However, the traditional peripheral security measures do not comply with the lightweight security requirements of the IoT ecosystem. Considering this, we propose a lightweight intrusion detection model for IoT networks (LIDM-IoT) that demonstrates similar efficiency in exposing malicious activities compared with the existing computationally expensive methods. The crux of the proposed model is that it provides efficient attack detection with lower computational requirements in IoT networks. LIDM-IoT achieves the feat through a novel unified feature selection strategy that unifies filter-based and embedded feature selection methods. The proposed feature selection strategy reduces the feature space by 94%. Also, we use only the records of a single attack type to build the model using the XGBoost algorithm. We have tested LIDM-IoT with unseen attack types to ensure its generalized behavior. The results indicate that the proposed model exhibits efficient attack detection, with a reduced feature set, in IoT networks compared with the state-of-the-art models.

物联网(IoT)在不同领域的广泛应用,使越来越多的 "物 "获得了 IP 连接。这些 "物 "在技术上被称为物联网设备,容易受到各种安全威胁。因此,在过去 5 年里,物联网恶意软件呈指数级增长,而确保物联网设备免受此类攻击是当今时代亟待解决的问题。然而,传统的外围安全措施并不符合物联网生态系统的轻量级安全要求。有鉴于此,我们提出了一种适用于物联网网络的轻量级入侵检测模型(LIDM-IoT),与现有的计算成本高昂的方法相比,该模型在揭露恶意活动方面具有类似的效率。所提模型的关键在于,它能在物联网网络中以较低的计算要求提供高效的攻击检测。LIDM-IoT 通过一种新颖的统一特征选择策略实现了这一壮举,该策略统一了基于过滤器的特征选择方法和嵌入式特征选择方法。所提出的特征选择策略将特征空间缩小了 94%。此外,我们仅使用单一攻击类型的记录来使用 XGBoost 算法建立模型。我们用未见过的攻击类型对 LIDM-IoT 进行了测试,以确保其通用性。结果表明,与最先进的模型相比,所提出的模型在物联网网络中以较少的特征集实现了高效的攻击检测。
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International Journal of Network Management
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