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    Optimized scheme for efficient and scalable key management in IEEE 802.16e-based networks by Sadeghi, Mohammad Mehdi Gilanian

    Published 2015
    “…To generate, update and distribute the keys for secure communication over IEEE 802.16e, the MBS applies Multicast and Broadcast Rekeying Algorithm (MBRA) as a basic key management algorithm. …”
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    Enhanced dynamic security assessment for power system under normal and fake tripping contingencies. by A. Salih, Qusay

    Published 2019
    “…The hybrid logistic model tree (hybrid LMT) approach proposed in this study combines the symmetrical uncertainties (SU) algorithm and the logistic model tree (LMT) algorithm. …”
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    Enhancing a cluster-based TDMA MAC protocol for vehicle-to-vehicle communications / Abubakar Bello Tambawal by Abubakar Bello , Tambawal

    Published 2020
    “…A dynamic time slot allocation strategy using a binary tree algorithm has been proposed to prevent vehicles from the adjacent clusters to reserve the same time slot. …”
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    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

    Published 2020
    “…The recorded kappa statistic value of 0.9802, and the Receiver Operating Curve (ROC) area of 98.73 %. The Random Tree relay algorithm model presented an improved average trip decision time of 18 ms compared with the standard minimum value of 20 ms recorded for the conventional relay due to eliminated communication channels. …”
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    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A.

    Published 2022
    “…It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Ahmed Dheyab, Saad, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

    Published 2022
    “…It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

    Published 2023
    “…It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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    Development of a Reliable Multicast Protocol in Mobile Ad Hoc Networks by Alahdal, Tariq A. A.

    Published 2008
    “…The first algorithm is developed to avoid the buffer overflow in the FS nodes. …”
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    A simultaneous spam and phishing attack detection framework for short message service based on text mining approach by Mohd Foozy, Cik Feresa

    Published 2017
    “…There are five (5) Classification techniques used such as Naive Bayes, K-NN, Decision Tree, Random Tree and Decision Stump. The result of Hybrid Feature accuracy using Rapidminer and Naive Bayes technique is 77.47%, for K-NN: 78.56%, Decision Tree: 57.16%, Random Tree: 57.24% and Decision Stump: 57.16%. …”
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