Internet of Things最新文献_第2页

A combination learning framework to uncover cyber attacks in IoT networks 揭示物联网网络攻击的组合学习框架

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-23 DOI: 10.1016/j.iot.2024.101395

Arati Behera , Kshira Sagar Sahoo , Tapas Kumar Mishra , Monowar Bhuyan

{"title":"A combination learning framework to uncover cyber attacks in IoT networks","authors":"Arati Behera , Kshira Sagar Sahoo , Tapas Kumar Mishra , Monowar Bhuyan","doi":"10.1016/j.iot.2024.101395","DOIUrl":"10.1016/j.iot.2024.101395","url":null,"abstract":"<div><div>The Internet of Things (IoT) is rapidly expanding, connecting an increasing number of devices daily. Having diverse and extensive networking and resource-constrained devices creates vulnerabilities to various cyber-attacks. The IoT with the supervision of Software Defined Network (SDN) enhances the network performance through its flexibility and adaptability. Different methods have been employed for detecting security attacks; however, they are often computationally efficient and unsuitable for such resource-constraint environments. Consequently, there is a significant requirement to develop efficient security measures against a range of attacks. Recent advancements in deep learning (DL) models have paved the way for designing effective attack detection methods. In this study, we leverage Genetic Algorithm (GA) with a correlation coefficient as a fitness function for feature selection. Additionally, mutual information (MI) is applied for feature ranking to measure their dependency on the target variable. The selected optimal features were used to train a hybrid DNN model to uncover attacks in IoT networks. The hybrid DNN integrates Convolutional Neural Network, Bi-Gated Recurrent Units (Bi-GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM) for training the input data. The performance of our proposed model is evaluated against several other baseline DL models, and an ablation study is provided. Three key datasets InSDN, UNSW-NB15, and CICIoT 2023 datasets, containing various types of attacks, were used to assess the performance of the model. The proposed model demonstrates an impressive accuracy and detection time over the existing model with lower resource consumption.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

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

Zainab Alwaisi , Tanesh Kumar , Erkki Harjula , Simone Soderi

{"title":"Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention","authors":"Zainab Alwaisi , Tanesh Kumar , Erkki Harjula , Simone Soderi","doi":"10.1016/j.iot.2024.101398","DOIUrl":"10.1016/j.iot.2024.101398","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Agostinho da Silva , Antonio J. Marques Cardoso

{"title":"Enhancing customer satisfaction through IIoT-Enabled coopetition: Strategic insights and impacts","authors":"Agostinho da Silva , Antonio J. Marques Cardoso","doi":"10.1016/j.iot.2024.101408","DOIUrl":"10.1016/j.iot.2024.101408","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Design of a turbo-based deep semantic autoencoder for marine Internet of Things","authors":"Xiaoling Han , Bin Lin , Nan Wu , Ping Wang , Zhenyu Na , Miyuan Zhang","doi":"10.1016/j.iot.2024.101393","DOIUrl":"10.1016/j.iot.2024.101393","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"An IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management","authors":"Nguyen-Ngan-Ha Lam , Chiao-Hsin Lin , Yi-Lu Li , Wei-Siang Ciou , Yi-Chun Du","doi":"10.1016/j.iot.2024.101411","DOIUrl":"10.1016/j.iot.2024.101411","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Bokolo Anthony Jnr

{"title":"Artificial intelligence of things and distributed technologies as enablers for intelligent mobility services in smart cities-A survey","authors":"Bokolo Anthony Jnr","doi":"10.1016/j.iot.2024.101399","DOIUrl":"10.1016/j.iot.2024.101399","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"LPF-IVN: A lightweight privacy-enhancing scheme with functional mechanism of intelligent vehicle networking","authors":"Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu","doi":"10.1016/j.iot.2024.101400","DOIUrl":"10.1016/j.iot.2024.101400","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Alexander Magyari, Yuhua Chen

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

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

Kanak Kumar , Anshul Verma , Pradeepika Verma

{"title":"IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen","authors":"Kanak Kumar , Anshul Verma , Pradeepika Verma","doi":"10.1016/j.iot.2024.101396","DOIUrl":"10.1016/j.iot.2024.101396","url":null,"abstract":"<div><div>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 <span><math><mrow><mn>150</mn><mo>×</mo><mn>9</mn><mo>=</mo><mn>1350</mn></mrow></math></span> 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 <span><math><mrow><mn>6</mn><mo>.</mo><mn>62</mn><mo>×</mo><mn>10</mn><mo>−</mo><mn>7</mn></mrow></math></span>, while CNG has the highest MSE of <span><math><mrow><mn>3</mn><mo>.</mo><mn>60</mn><mo>×</mo><mn>10</mn><mo>−</mo><mn>4</mn></mrow></math></span>. This system can intelligently preserve gases, ensure safety precautions, and prevent accidents in the kitchens.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Fed-Evolver: An automated evolving approach for federated Intrusion Detection System using adversarial autoencoder in SDN-enabled networks","authors":"Phan The Duy, Do Thi Thu Hien, Tran Duc Luong, Nguyen Huu Quyen, Van-Hau Pham","doi":"10.1016/j.iot.2024.101397","DOIUrl":"10.1016/j.iot.2024.101397","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0