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  • Open Access

    ARTICLE

    Renovated Random Attribute-Based Fennec Fox Optimized Deep Learning Framework in Low-Rate DoS Attack Detection in IoT

    Prasanalakshmi Balaji1,2, Sangita Babu3, Maode Ma4, Zhaoxi Fang2, Syarifah Bahiyah Rahayu5,6,*, Mariyam Aysha Bivi1, Mahaveerakannan Renganathan7

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5831-5858, 2025, DOI:10.32604/cmc.2025.065260 - 30 July 2025

    Abstract The rapid progression of the Internet of Things (IoT) technology enables its application across various sectors. However, IoT devices typically acquire inadequate computing power and user interfaces, making them susceptible to security threats. One significant risk to cloud networks is Distributed Denial-of-Service (DoS) attacks, where attackers aim to overcome a target system with excessive data and requests. Among these, low-rate DoS (LR-DoS) attacks present a particular challenge to detection. By sending bursts of attacks at irregular intervals, LR-DoS significantly degrades the targeted system’s Quality of Service (QoS). The low-rate nature of these attacks confuses their… More >

  • Open Access

    ARTICLE

    Spectrum Sensing Using Optimized Deep Learning Techniques in Reconfigurable Embedded Systems

    Priyesh Kumar*, Ponniyin Selvan

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2041-2054, 2023, DOI:10.32604/iasc.2023.030291 - 05 January 2023

    Abstract The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods… More >

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