Internet of Things最新文献_第3页

Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention 确保受限物联网系统的安全：异常检测和预防的轻量级机器学习方法

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-21 DOI: 10.1016/j.iot.2024.101398

Zainab Alwaisi , Tanesh Kumar , Erkki Harjula , Simone Soderi

With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.

随着物联网、边缘和雾计算等先进技术的发展，网络攻击变得越来越复杂。物联网网络促进了各个领域的协作和智能任务，包括工业 4.0、数字医疗和家庭自动化。然而，物联网设备的激增引发了对严重攻击的担忧，特别是那些针对能源和内存等资源限制的攻击。为了应对这些挑战，微型机器学习（TinyML）已成为一个新的研究领域，其重点是为嵌入式和物联网系统量身定制的机器学习技术。本研究提出了一种 ML 检测机制，旨在对物联网设备中的资源受限攻击进行分类和检测。我们将物联网设备视为边缘计算和云计算连续体中不可分割的组成部分，利用 EdgeML 和 CloudML 进行检测。我们的论文对 ML 模型进行了比较分析，重点关注物联网应用中的能耗和内存使用情况。我们比较了各种 ML 方法，包括基于云、基于边缘和基于设备的训练和检测策略。评估包括将这些 ML 技术应用于使用 TinyML 的小型物联网设备以及云和边缘设备。我们的研究结果表明，部署在智能设备上的决策树算法在训练效率、资源利用率以及检测物联网设备上资源受限攻击的能力方面都优于其他方法。在检测物联网系统中的资源受限攻击方面，我们展示了所有提出的 ML 模型的高准确率，超过 96.9%。总之，这项研究为在物联网及相关技术的动态环境中实施有效的安全措施提供了指导。

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引用次数: 0

Enhancing customer satisfaction through IIoT-Enabled coopetition: Strategic insights and impacts 通过物联网合作竞争提高客户满意度：战略见解和影响

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-20 DOI: 10.1016/j.iot.2024.101408

Agostinho da Silva , Antonio J. Marques Cardoso

This research investigates the significant role of Industrial Internet of Things (IIoT) to enable Coopetition Network Practices (CNPs) in enhancing the performance of Small and Medium Enterprises (SMEs) within the context of global digital supply chains. Employing a quantitative approach, our study reveals that CNPs contribute to a noTable 51.0 % improvement in factors determining customer satisfaction. This underscores the strategic importance of blending competition with collaboration to refine production processes and align with consumer expectations. Additionally, the research presents a remarkable 69.1 % boost in operational consistency and reports substantial progress in manufacturing flexibility and the value-to-weight ratio, witnessing increases of 125.8 % and 33.2 %, respectively. These improvements are pivotal in optimizing production resources, which in turn, have led to a 29.3 % decrease in customer complaints and a 15.6 % rise in on-time delivery rates. Conversely, a slight decline in the consistency of the value-to-weight ratio was observed, pointing to potential areas for future research. The findings decisively show that CNPs offer concrete advantages by enhancing customer satisfaction determinants and operational efficiency in SMEs. The paper advocates for future studies to directly measure customer satisfaction and to formulate actionable guidelines for the effective implementation of coopetition strategies. This proposed research direction aims to provide solutions to the manufacturing sector's emerging challenges, thereby promoting competitive advantage and growth in the digital era.

本研究探讨了在全球数字供应链背景下，工业物联网（IIoT）在促进合作网络实践（CNPs）以提高中小企业（SMEs）绩效方面的重要作用。我们的研究采用定量方法，揭示了合作网络实践对提高客户满意度的贡献率高达 51.0%。这凸显了将竞争与合作相结合，以完善生产流程并满足消费者期望的战略重要性。此外，研究还显示，运营一致性显著提高了 69.1%，生产灵活性和价值重量比也取得了长足进步，分别提高了 125.8% 和 33.2%。这些改进在优化生产资源方面发挥了关键作用，进而使客户投诉减少了 29.3%，准时交货率提高了 15.6%。与此相反，价值重量比的一致性略有下降，这为今后的研究指明了潜在的领域。研究结果明确显示，CNP 通过提高中小企业的客户满意度决定因素和运营效率，提供了具体的优势。本文主张今后的研究应直接衡量客户满意度，并为有效实施合作竞争战略制定可操作的指导方针。这一拟议的研究方向旨在为制造业新出现的挑战提供解决方案，从而促进数字时代的竞争优势和增长。

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引用次数: 0

Design of a turbo-based deep semantic autoencoder for marine Internet of Things 为海洋物联网设计基于涡轮的深度语义自动编码器

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-20 DOI: 10.1016/j.iot.2024.101393

Xiaoling Han , Bin Lin , Nan Wu , Ping Wang , Zhenyu Na , Miyuan Zhang

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

随着全球海洋经济的快速增长和海上活动的蓬勃开展，海洋物联网（IoT）正获得前所未有的发展势头。然而，目前的海洋设备在数据传输效率和语义理解方面存在不足。为解决这些问题，本文提出了一种新型端到端（E2E）编码方案，即基于 Turbo 的深度语义自动编码器（Turbo-DSA）。Turbo-DSA 通过发射器和接收器的 E2E 设计实现了语义层面的源信道联合编码，同时学会适应环境变化。语义编码器和解码器由转换器技术组成，可有效地将信息转换成语义向量。在神经网络训练过程中，这些向量会根据信道特性和背景知识库进行动态调整。Turbo 结构进一步增强了语义向量。具体来说，信道编码器利用 Turbo 结构分离语义向量，确保意义的精确传递，而信道解码器则利用 Turbo 迭代解码优化语义向量的表示。目标函数、语义提取和整个训练过程的设计确保了变换器与 Turbo 结构的深度融合。与传统的 Turbo 编码技术相比，Turbo-DSA 的收敛速度更快，这要归功于它对语义向量的高效处理。仿真结果表明，Turbo-DSA 在关键性能指标上超越了现有基准，如双语评估底层研究得分和句子相似度。这一点在低信噪比条件下尤为明显，它显示出卓越的文本语义传输效率和对多变海洋信道环境的适应性。

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引用次数: 0

An IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management 支持物联网的脑电图耳机，可为慢性耳鸣评估和症状管理定制音乐

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-19 DOI: 10.1016/j.iot.2024.101411

Nguyen-Ngan-Ha Lam , Chiao-Hsin Lin , Yi-Lu Li , Wei-Siang Ciou , Yi-Chun Du

Chronic tinnitus often affects elderly or hearing-impaired individuals, which can disturb their daily lives by disrupting concentration and limiting communication. Clinically, sound masking using external sounds like white noise (WN) aims to mask tinnitus and relieve secondary symptoms. Even when symptoms are relieved, tinnitus often requires long-term management, and for patients to visit healthcare professionals regularly. Generally, it could make maintaining symptom management challenging due to the time and effort required for consistent follow-ups. EEG is considered as one of the objective marker for assessing tinnitus symptoms. In this study, we designed IoT-enabled EEG sensing (IEES) headphones, an innovative IoT device that provided customized music (CM) and EEG measurement. The headphones employed a pitch-matching (PM) method to create CM tailored to each patient at specific frequencies for tinnitus patients. To collect EEG measurements, the device incorporated OpenBCI electrodes and a sensing chip to monitor brain waves and evaluate the outcomes.. After 30 days of experiment, participants showed significant reductions in both tinnitus handicap inventory (THI) scores and visual analog scale for annoyance (VAS-A) scores. In comparison, tinnitus frequency showed a slight reduction. EEG measurements demonstrated an increase in alpha band activity. In questionnaires, patients reported high satisfaction with their experiences. These findings highlight the potential of the proposed method for chronic tinnitus assessment and symptom management.

慢性耳鸣常常影响老年人或听力受损的人，会扰乱他们的注意力，限制交流，从而影响日常生活。在临床上，使用白噪声（WN）等外部声音进行声音掩蔽的目的是掩蔽耳鸣，缓解继发性症状。即使症状得到缓解，耳鸣通常也需要长期治疗，患者需要定期到医疗机构就诊。一般来说，由于持续随访所需的时间和精力，这可能会使维持症状管理具有挑战性。脑电图被认为是评估耳鸣症状的客观指标之一。在这项研究中，我们设计了支持物联网的脑电图传感（IEES）耳机，这是一款创新的物联网设备，可提供定制音乐（CM）和脑电图测量。该耳机采用音高匹配（PM）方法，为每位耳鸣患者量身定制特定频率的音乐。为了收集脑电图测量结果，该设备集成了 OpenBCI 电极和传感芯片，用于监测脑电波和评估结果。经过 30 天的实验，参与者的耳鸣障碍量表（THI）评分和恼人程度视觉模拟量表（VAS-A）评分均有显著下降。相比之下，耳鸣频率略有降低。脑电图测量显示阿尔法波段活动增加。在问卷调查中，患者对自己的经历表示非常满意。这些发现凸显了该方法在慢性耳鸣评估和症状管理方面的潜力。

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引用次数: 0

Artificial intelligence of things and distributed technologies as enablers for intelligent mobility services in smart cities-A survey 人工智能和分布式技术作为智慧城市智能交通服务的推动因素--一项调查

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-18 DOI: 10.1016/j.iot.2024.101399

Bokolo Anthony Jnr

The society is witnessing an accelerated large-scale adoption of technology with transformative effects on daily transport operations, with cities now depending on data driven mobility services. Disruptive technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and decentralized technologies for example Distributed Ledger Technologies (DLT) are being deployed in smart cities. However, AI is faced with data security and privacy issues due to its centralized mode of deployment. Conversely, DLT which employs a decentralized architecture can be converged with AI to provide a secure data sharing across various IoT thereby overcoming the existing setbacks faced in deploying AI in smart cities. Evidently, the convergence of AI and IoT as AIoT and DLT have great potential to create novel business models for improved data driven services such as intelligent mobility in smart cities. Although research on the convergence of AI, IoT and DLT exists, our understanding of its integration in achieving intelligent mobility services in smart cities remains fragmented as current research in this area remains scarce. This study bridges the gap between theory and practice by providing researchers and practitioners with insights on the potential benefits of converging AIoT and DLT. Grounded on the Technology Organization Environment (TOE) framework this study presents the technological, organizational, and environmental factors that impacts the convergence of AIoT and DLT in smart cities. Additionally, findings from this study present use cases on the applicability of AIoT and DLT to support intelligent mobility services in smart cities.

社会正在加速大规模采用对日常交通运营具有变革性影响的技术，城市现在依赖于数据驱动的交通服务。人工智能（AI）、物联网（IoT）等颠覆性技术以及分布式账本技术（DLT）等去中心化技术正在被部署到智慧城市中。然而，人工智能因其集中式部署模式而面临数据安全和隐私问题。相反，采用去中心化架构的 DLT 可以与人工智能融合，在各种物联网中提供安全的数据共享，从而克服在智慧城市中部署人工智能所面临的现有挫折。显而易见，人工智能与物联网的融合（如 AIoT 和 DLT）具有巨大潜力，可为改进数据驱动型服务（如智慧城市中的智能移动性）创造新的商业模式。尽管存在关于人工智能、物联网和数字签名技术融合的研究，但我们对其在实现智慧城市智能交通服务方面的融合的理解仍然是零散的，因为目前该领域的研究仍然很少。本研究弥补了理论与实践之间的差距，为研究人员和从业人员提供了有关人工智能物联网和数字地图技术融合潜在益处的见解。本研究以技术组织环境（TOE）框架为基础，介绍了影响智能城市中人工智能技术和数字签名技术融合的技术、组织和环境因素。此外，本研究还介绍了人工智能技术和数字签名技术在支持智慧城市智能交通服务方面的应用案例。

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引用次数: 0

LPF-IVN: A lightweight privacy-enhancing scheme with functional mechanism of intelligent vehicle networking LPF-IVN：具有智能车联网功能机制的轻量级隐私增强方案

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-17 DOI: 10.1016/j.iot.2024.101400

Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu

Due to decentralization and effective prevention of privacy leakage, Differential Private Federated Learning(DP-FL) has emerged as an efficient technique in the Internet of Vehicles (IoV). However, the essence of key industrial is big data. When applying the DP-FL model to the IoV, these large-scale nonlightweight data such as Non-IID and high-dimensional will decrease the security and accuracy of the model. Therefore, for the security and accuracy of the IoV, we proposed a lightweight DP-FL framework called DPF-IVN, considering the impact of heterogeneous and privacy leak in the context of IoV. It adopts the idea of “lowering dimension first and then optimization” to process non-light quantified data in the IoV. Specifically, we novelly design a Federated Randomized Principal Component Analysis (FRPCA) algorithm, allowing users to map local data to low-dimensional data. Then, we propose the Functional Mechanism(FM) to disturb the gradient parameters to solve the problem of low training accuracy caused by gradient cutting. Besides, to reduce model differences, we used the Bregman dispersal as a regularized item update loss function to improve the accuracy of the model. Extensive experiments demonstrate the superior performance of DPF-IVN in the heterogeneous environment compared to other methods.

由于去中心化和有效防止隐私泄露，差分私有联合学习（DP-FL）已成为车联网（IoV）中的一种有效技术。然而，关键产业的本质是大数据。将DP-FL模型应用于车联网时，这些非IID、高维等大规模非轻量级数据会降低模型的安全性和准确性。因此，考虑到物联网背景下异构和隐私泄露的影响，为了物联网的安全性和准确性，我们提出了一种名为 DPF-IVN 的轻量级 DP-FL 框架。它采用 "先降维、后优化 "的思路来处理物联网中的非轻量化数据。具体来说，我们新颖地设计了一种联邦随机主成分分析（Federated Randomized Principal Component Analysis，FRPCA）算法，允许用户将本地数据映射为低维数据。然后，我们提出了扰乱梯度参数的功能机制（FM），以解决梯度切割导致的训练精度低的问题。此外，为了减少模型差异，我们使用 Bregman 分散作为正则化项更新损失函数，以提高模型的准确性。大量的实验证明，与其他方法相比，DPF-IVN 在异构环境中的性能更加优越。

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引用次数: 0

Securing the Internet of Things with Ascon-Sign 利用 Ascon-Sign 保障物联网安全

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-16 DOI: 10.1016/j.iot.2024.101394

Alexander Magyari, Yuhua Chen

With a Cryptographically-Relevant Quantum Computer (CRQC) estimated to be viable within the next 15 years, the development of post-quantum security is imperative. Previously secure networks may soon fall victim to these CRQCs as they will likely attack the weakest link in a network. In modern networks, these weak-links are often present in the form of Internet of Things (IoT) devices, as the resource constrains imposed by these wireless nodes leads to lowered security. We offer the first Ascon-Sign implementation for resource constrained FPGAs, which allows a wireless sensor network to verify nodes. Our design runs twice as fast as similarly-area constrained devices, and shows a 33% reduction in power per operation. We demonstrate the capability of our design by integrating it with a wireless sensor network for weather detecting. We also propose an amendment to the Ascon-Sign specification that allows for shortened processing time and lower memory requirements.

据估计，与加密相关的量子计算机（CRQC）将在未来 15 年内问世，因此开发后量子安全技术势在必行。以前安全的网络可能很快就会成为这些 CRQC 的牺牲品，因为它们很可能会攻击网络中最薄弱的环节。在现代网络中，这些薄弱环节通常以物联网（IoT）设备的形式存在，因为这些无线节点施加的资源限制导致安全性降低。我们首次为资源受限的 FPGA 实现了 Ascon-Sign，使无线传感器网络能够验证节点。我们的设计运行速度是类似区域受限器件的两倍，每次运行功耗降低了 33%。我们将我们的设计与用于天气检测的无线传感器网络相集成，从而展示了我们的设计能力。我们还对 Ascon-Sign 规范提出了修改建议，从而缩短了处理时间，降低了内存要求。

引用次数: 0

IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen IoT-HGDS：基于物联网集成机器学习的智能厨房有害气体检测系统

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-15 DOI: 10.1016/j.iot.2024.101396

Kanak Kumar , Anshul Verma , Pradeepika Verma

This paper proposes an Internet of Things (IoT) and Machine Learning (ML) integrated Hazardous Gas Detection System (IoT-HGDS) for smart kitchens. The design incorporates six tin-oxide-based metal–oxide–semiconductor (MOS) gas sensor arrays and one DHT22 (temperature & humidity sensor). This IoT-HGDS can detect different hazardous gases, Volatile Organic Compounds (VOCs), and odors responses released from the kitchen’s materials and transmit them to a Remote Data Processing Centre (RDPC) through Amazon-Web Services (AWS) in real time. In this experiment, we collected

150 \times 9 = 1350

samples from 9 kitchen materials like ghee, milk, liquid petroleum gas (LPG), bread, mustard oil, compressed natural gas (CNG), pigeon peas, refined oil, and kerosene. The Standardized Independent Component Analysis (SICA) pre-processing technique has been used to clean data, standardize the features, and remove outliers. ML approaches like Logistic Regression (LR), Adaptive Boosting (AdaBoost) and Regularized Discriminant Analysis (RDA) have been applied for accurate identification of gases/VOCs class and provide immediate alerts to improve kitchen safety. The SICA-RDA classifier outperformed (highest accuracy at 97.78 %) as compared to LR and AdaBoost in terms of performance and balanced precision, recall, and F1-Score. LR has the lowest performance in all metrics. LPG has the lowest Mean Squared Error (MSE) of

6.62 \times 10 - 7

, while CNG has the highest MSE of

3.60 \times 10 - 4

. This system can intelligently preserve gases, ensure safety precautions, and prevent accidents in the kitchens.

本文提出了一种用于智能厨房的物联网（IoT）和机器学习（ML）集成式有害气体检测系统（IoT-HGDS）。该设计集成了六个锡氧化物基金属氧化物半导体（MOS）气体传感器阵列和一个 DHT22（温度和湿度传感器）。这种物联网-HGDS 可以检测厨房材料释放的不同有害气体、挥发性有机化合物（VOC）和气味反应，并通过亚马逊网络服务（AWS）实时传输到远程数据处理中心（RDPC）。在本实验中，我们从酥油、牛奶、液化石油气（LPG）、面包、芥末油、压缩天然气（CNG）、豌豆、精炼油和煤油等 9 种厨房用品中采集了 150×9=1350 个样本。标准化独立成分分析（SICA）预处理技术用于清理数据、标准化特征和去除异常值。逻辑回归（LR）、自适应提升（AdaBoost）和正则判别分析（RDA）等 ML 方法已被用于准确识别气体/VOC 类别，并提供即时警报，以提高厨房安全。与 LR 和 AdaBoost 相比，SICA-RDA 分类器在性能和均衡精度、召回率和 F1 分数方面表现更优（最高精度为 97.78%）。在所有指标中，LR 的性能最低。液化石油气的平均平方误差（MSE）最低，为 6.62×10-7，而压缩天然气的平均平方误差（MSE）最高，为 3.60×10-4。该系统可以智能地保存气体，确保安全防范措施，防止厨房事故的发生。

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引用次数: 0

Fed-Evolver: An automated evolving approach for federated Intrusion Detection System using adversarial autoencoder in SDN-enabled networks Fed-Evolver：在支持 SDN 的网络中使用对抗性自动编码器的联合入侵检测系统自动演进方法

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-14 DOI: 10.1016/j.iot.2024.101397

Phan The Duy, Do Thi Thu Hien, Tran Duc Luong, Nguyen Huu Quyen, Van-Hau Pham

Intrusion Detection Systems (IDS) have garnered escalating significance in response to the evolving landscape of cyberattacks, driven by the adaptability and versatility of Software Defined Networking (SDN)-based networks in enhancing security orchestration. Although Machine Learning (ML) models have been developed for IDS, they require large amounts of labeled data to achieve high performance. However, acquiring labels for attacks is a time-consuming process and can cause problems in deploying the existing ML models in new systems or lower performance due to a shortage of labeled data on pre-trained datasets. Additionally, such ML-based IDS models lack the self-learning function to automatically adapt to new cyberattacks during network operations. To overcome these challenges, our work proposes Fed-Evolver, an automated evolving approach for federated IDS that combines Generative Adversarial Networks (GANs) with Auto Encoder (AE) and a semi-supervised adversarial Autoencoder (SSAAE) for spotting intrusion actions. Our Fed-Evolver leverages supervised and unsupervised learning strategies to build efficient IDS models in the context of labeled data scarcity with the help of Federated Learning (FL). It allows data owners to collaborate for training intrusion detection models to provide the self-evolving capability in SDN-enabled networks. Our proposed framework is evaluated on 6 cyberattack datasets, including CICIDS2018, CIC-ToN-IoT, NF-UNSW-NB15, InSDN, InSecLab-IDS2021, DNP3 Intrusion Detection, and it outperforms other ML methods even when trained with only 1% proportion of labeled data, achieving consistently high performance across all metrics on the datasets.

由于基于软件定义网络（SDN）的网络在增强安全协调方面的适应性和多功能性，入侵检测系统（IDS）在应对不断变化的网络攻击方面的重要性不断提升。虽然已开发出用于 IDS 的机器学习 (ML) 模型，但它们需要大量标记数据才能实现高性能。然而，获取攻击标签是一个耗时的过程，可能会导致在新系统中部署现有 ML 模型时出现问题，或由于缺乏预训练数据集上的标签数据而降低性能。此外，这种基于 ML 的 IDS 模型缺乏自学习功能，无法在网络运行期间自动适应新的网络攻击。为了克服这些挑战，我们的工作提出了 Fed-Evolver，这是一种用于联合 IDS 的自动演进方法，它将生成式对抗网络（GAN）与自动编码器（AE）和半监督对抗自动编码器（SSAAE）相结合，用于发现入侵行为。我们的 Fed-Evolver 利用监督和非监督学习策略，在联邦学习（FL）的帮助下，在标记数据稀缺的情况下建立高效的 IDS 模型。它允许数据所有者合作训练入侵检测模型，从而在支持 SDN 的网络中提供自适应能力。我们提出的框架在 6 个网络攻击数据集（包括 CICIDS2018、CIC-ToN-IoT、NF-UNSW-NB15、InSDN、InSecLab-IDS2021、DNP3 入侵检测）上进行了评估，结果表明，即使仅使用 1%比例的标记数据进行训练，该框架也优于其他 ML 方法，并在数据集的所有指标上实现了持续的高性能。

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引用次数: 0

Automated image-based fire detection and alarm system using edge computing and cloud-based platform 使用边缘计算和云平台的基于图像的火灾自动探测和报警系统

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-10-10 DOI: 10.1016/j.iot.2024.101402

Xueliang Yang, Yenchun Li, Qian Chen

To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.

为应对日益严峻的野火挑战，本文介绍了一种基于图像的火灾自动探测和报警系统，该系统利用边缘计算和云平台，专门用于城市建筑火灾探测。该系统通过红外热像仪捕捉 RGB 和红外图像，并利用现有的计算机视觉技术检测火焰和烟雾等火灾特征。通过集成边缘计算，该系统最大限度地减少了延迟，从而提高了火灾探测和警报启动的准确性。云平台支持广泛的数据存储、分析和远程监控，可确保数据的可访问性和可扩展的数据管理。拟议的系统说明包括详细的系统架构设计、数据收集以及利用 RGB 和热图像数据进行火灾探测的探测算法的选择和应用。利用校园建筑和周边风险易发区域作为测试平台，所提议的系统展示了所需的火灾探测能力，以及在城市区域内快速识别和应对火灾事故的强大解决方案。所提议的系统功能可扩展并适用于其他火灾易发区域。

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