An Efficient Technique for Identifying Distributed Denial of‎Service Active Assaults Using Deep Neural Networks Based on the ‎Adaptive System Intelligence Paradigm

  • Authors

    • Karthikeyan Kaliyaperumal Post Doc Researcher, Lincoln University College, Malaysia
    • Raja Sarath Kumar Boddu Professor in CSE, Raghu Engineering College, Visakhapatnam, India
    • Sai Kiran Oruganti Professor, Faculty of Engineering and Built Science, Lincoln University College-KL Malaysia
    • Guidsa Tesema Kebesa Dean, School of Informatics and Electrical Engineering, IoT – HH Campus, Ambo University, Ambo, Ethiopia
    • Mohsen Aghaeiboorkheili Head of School, Mathematics and Computer Science, PNG University of Technology, Lae, Papua New Guinea
    • Rajendran Bhojan School of Mathematics and Computer Science, PNG University of Technology, Lae, Papua New Guinea
    https://doi.org/10.14419/dwfxsc41

    Received date: May 30, 2025

    Accepted date: June 24, 2025

    Published date: July 3, 2025

  • Active Attacks; Detection; Deep Neural Network; DDoS; Optimization; Adaptive Learning

  • Abstract

    A collection of interconnected devices that exchange data online is known as the Internet of Things. The IoT environment's diverse components make the distributed denial-of-service attack a security risk. One of the most important tasks in creating a smarter environment for ‎end users is detecting DDoS attacks in the Internet of Things. A new version of the optimized Elman recurrent neural network (ERNN) is ‎proposed to detect DDoS active attacks in Internet of Things scenarios. The proposed detection approach optimizes the weight and bias of ‎ERNN (ABCO-ERNN) using a novel adaptive bacterial colony optimization (ABCO) technique. The ABCO algorithm uses an adaptable ‎step size to increase the BCO's capacity for both exploration and exploitation. The four datasets, BoT-IoT, CIC-IDS2017, CIC-DDoS2019, ‎and IoTID20, are used to compare performance, and five distinct performance measures, including accuracy, precision, sensitivity, specificity-‎ty, and f-measure, are considered. When compared to previous literature algorithms, the proposed ABCO-ERNN detection approach ‎produced a high detection rate according to the experimental results‎.

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    Kaliyaperumal, K. ., Boddu, R. S. K. . ., Oruganti , S. K. ., Kebesa , G. T. ., Aghaeiboorkheili , M. ., & Bhojan, R. . (2025). An Efficient Technique for Identifying Distributed Denial of‎Service Active Assaults Using Deep Neural Networks Based on the ‎Adaptive System Intelligence Paradigm. International Journal of Basic and Applied Sciences, 14(2), 577-590. https://doi.org/10.14419/dwfxsc41