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  • Open Access

    ARTICLE

    A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

    Chandraumakantham Om Kumar1,*, Sudhakaran Gajendran2, Suguna Marappan1, Mohammed Zakariah3, Abdulaziz S. Almazyad4

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 471-501, 2024, DOI:10.32604/cmc.2024.054966 - 15 October 2024

    Abstract The security of the wireless sensor network-Internet of Things (WSN-IoT) network is more challenging due to its randomness and self-organized nature. Intrusion detection is one of the key methodologies utilized to ensure the security of the network. Conventional intrusion detection mechanisms have issues such as higher misclassification rates, increased model complexity, insignificant feature extraction, increased training time, increased run time complexity, computation overhead, failure to identify new attacks, increased energy consumption, and a variety of other factors that limit the performance of the intrusion system model. In this research a security framework for WSN-IoT, through… More >

  • Open Access

    ARTICLE

    SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

    Ana María Peco Chacón, Isaac Segovia Ramírez, Fausto Pedro García Márquez*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2595-2608, 2023, DOI:10.32604/iasc.2023.037277 - 11 September 2023

    Abstract Maintenance operations have a critical influence on power generation by wind turbines (WT). Advanced algorithms must analyze large volume of data from condition monitoring systems (CMS) to determine the actual working conditions and avoid false alarms. This paper proposes different support vector machine (SVM) algorithms for the prediction and detection of false alarms. K-Fold cross-validation (CV) is applied to evaluate the classification reliability of these algorithms. Supervisory Control and Data Acquisition (SCADA) data from an operating WT are applied to test the proposed approach. The results from the quadratic SVM showed an accuracy rate of More >

  • Open Access

    ARTICLE

    Multi-Attack Intrusion Detection System for Software-Defined Internet of Things Network

    Tarcízio Ferrão1,*, Franklin Manene2, Adeyemi Abel Ajibesin3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4985-5007, 2023, DOI:10.32604/cmc.2023.038276 - 29 April 2023

    Abstract Currently, the Internet of Things (IoT) is revolutionizing communication technology by facilitating the sharing of information between different physical devices connected to a network. To improve control, customization, flexibility, and reduce network maintenance costs, a new Software-Defined Network (SDN) technology must be used in this infrastructure. Despite the various advantages of combining SDN and IoT, this environment is more vulnerable to various attacks due to the centralization of control. Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service (DDoS) attacks, but they often lack mechanisms to mitigate their severity. This paper proposes… More >

  • Open Access

    ARTICLE

    Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

    T. Jagadesh1,2, B. Sheela Rani3,*

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 449-463, 2023, DOI:10.32604/iasc.2023.026868 - 06 June 2022

    Abstract Interference is a key factor in radar return misdetection. Strong interference might make it difficult to detect the signal or targets. When interference occurs in the sidelobes of the antenna pattern, Sidelobe Cancellation (SLC) and Sidelobe Blanking are two unique solutions to solve this problem (SLB). Aside from this approach, the probability of false alert and likelihood of detection are the most essential parameters in radar. The chance of a false alarm for any radar system should be minimal, and as a result, the probability of detection should be high. There are several interference cancellation… More >

  • Open Access

    ARTICLE

    False Alarm Reduction in ICU Using Ensemble Classifier Approach

    V. Ravindra Krishna Chandar1,*, M. Thangamani2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 165-181, 2022, DOI:10.32604/iasc.2022.022339 - 15 April 2022

    Abstract

    During patient monitoring, false alert in the Intensive Care Unit (ICU) becomes a major problem. In the category of alarms, pseudo alarms are regarded as having no clinical or therapeutic significance, and thus they result in fatigue alarms. Artifacts are misrepresentations of tissue structures produced by imaging techniques. These Artifacts can invalidate the Arterial Blood Pressure (ABP) signal. Therefore, it is very important to develop algorithms that can detect artifacts. However, ABP has algorithmic shortcomings and limitations of design. This study is aimed at developing a real-time enhancement of independent component analysis (EICA) and time-domain

    More >

  • Open Access

    ARTICLE

    Structure Health Monitoring (SHM) System Trade Space Analysis

    Salman A. Albinali1, David R. Jacques2

    Structural Durability & Health Monitoring, Vol.10, No.1, pp. 1-17, 2014, DOI:10.3970/sdhm.2014.010.001

    Abstract An analytic approach to exploring the tradespace associated with Structural Health Monitoring (SHM) systems is presented. Modeling and simulation of the life cycle of a legacy aircraft and the expected operational and maintenance events that could occur is shown. A focus on the SHM system detection of a significant crack length and the possibility of False Alarm (FA), miss detection and mishap events is investigated. The modeling approach allows researchers to explore the tradespace associated with safe and critical crack lengths, sensor thresholds, scheduled maintenance intervals, falsely triggered maintenance actions, and mishaps due to missed… More >

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