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    ARTICLE

    A Detailed Study on IoT Platform for ECG Monitoring Using Transfer Learning

    Md Saidul Islam*
    Journal on Internet of Things, Vol.4, No.3, pp. 127-140, 2022, DOI:10.32604/jiot.2022.037489
    Abstract Internet of Things (IoT) technologies used in health have the potential to address systemic difficulties by offering tools for cost reduction while improving diagnostic and treatment efficiency. Numerous works on this subject focus on clarifying the constructs and interfaces between various components of an IoT platform, such as knowledge generation via smart sensors collecting biosignals from the human body and processing them via data mining and, in recent times, deep neural networks offered to host on cloud computing architecture. These approaches are intended to assist healthcare professionals in their daily activities. In this comparative research, More >

  • Open AccessOpen Access

    ARTICLE

    An Intrusion Detection Scheme Based on Federated Learning and Self-Attention Fusion Convolutional Neural Network for IoT

    Jie Deng1, Ran Guo2, Zilong Jin1,3,*
    Journal on Internet of Things, Vol.4, No.3, pp. 141-153, 2022, DOI:10.32604/jiot.2022.038914
    Abstract Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage, data privacy leaks, high communication costs, unsatisfactory detection rates, and false positive rate. To address existing issues in intrusion detection, this paper presents a novel approach called CS-FL, which combines Federated Learning and a Self-Attention Fusion Convolutional Neural Network. Federated Learning is a new distributed computing model that enables individual training of client data without uploading local data to a central server. at the same time, local training results are uploaded and integrated across all participating clients to produce a global… More >

  • Open AccessOpen Access

    ARTICLE

    Signature-Based Intrusion Detection System in Wireless 6G IoT Networks

    Mansoor Farooq1,*, Mubashir Hassan Khan2
    Journal on Internet of Things, Vol.4, No.3, pp. 155-168, 2022, DOI:10.32604/jiot.2022.039271
    Abstract An “Intrusion Detection System” (IDS) is a security measure designed to perceive and be aware of unauthorized access or malicious activity on a computer system or network. Signature-based IDSs employ an attack signature database to identify intrusions. This indicates that the system can only identify known attacks and cannot identify brand-new or unidentified assaults. In Wireless 6G IoT networks, signature-based IDSs can be useful to detect a wide range of known attacks such as viruses, worms, and Trojans. However, these networks have specific requirements and constraints, such as the need for real-time detection and low-power More >

  • Open AccessOpen Access

    ARTICLE

    TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

    Qi Liu1, Zihao Wu1,*, Xiaodong Liu2
    Journal on Internet of Things, Vol.4, No.3, pp. 169-181, 2022, DOI:10.32604/jiot.2022.040368
    Abstract This study proposed a lightweight but high-performance convolution network for accurately classifying five upper limb movements of arm, involving forearm flexion and rotation, arm extension, lumbar touch and no reaction state, aiming to monitoring patient’s rehabilitation process and assist the therapist in elevating patient compliance with treatment. To achieve this goal, a lightweight convolution neural network TMCA-Net (Time Multiscale Channel Attention Convolutional Neural Network) is designed, which combines attention mechanism, uses multi-branched convolution structure to automatically extract feature information at different scales from sensor data, and filters feature information based on attention mechanism. In particular,… More >

  • Open AccessOpen Access

    ARTICLE

    Evidence-Based Federated Learning for Set-Valued Classification of Industrial IoT DDos Attack Traffic

    Jiale Cheng1, Zilong Jin1,2,*
    Journal on Internet of Things, Vol.4, No.3, pp. 183-195, 2022, DOI:10.32604/jiot.2022.042054
    Abstract A novel Federated learning classifier is proposed using the Dempster-Shafer (DS) theory for the set-valued classification of industrial IoT Distributed Denial of Service (DDoS) attack traffic. The proposed classifier, referred to as the evidence-based federated learning classifier, employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service (DDoS) traffic from the local data of private industrial clients. The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer, situated on the federated server. Lastly, the set value classification task of attack More >

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