A Cybersecurity Threat Detection Using Advanced Neural Network Methodologies

  • Authors

    • Dheeraj Hebri Department of Master of Computer Applications, Srinivas Institute of Technology, Karnataka, India
    • Swagatika Devi Department of CSE, Institute of Technical Education and Research, Bhubaneswar, Odisha 751030, India.
    • Prabu Selvam School of Computing, SRM Institute of Science & Technology, Tiruchirapalli Campus, Tiruchirapalli, India
    • Valluri Venkata Gopala Rao Department of Computer Applications, Aditya University, Surampalem, Andhra Pradesh 533437, India
    • Robins A Kattoor Department of Computer Applications, Marian College Kuttikkanam Autonomous, Kerala 685531, India
    • M. Sridharan Department of Mathematics, NPR College of Engineering and Technology (Autonomous), Dindigul, India
    • B. Selvalakshmi Department of Computer Science and Engineering, Tagore Engineering College, Chennai, Tamil Nadu 600127, India
    • Arun Chokkalingam Department of Biomedical Engineering, Vels Institute of Science, Technology & Advanced Studies, Chennai, India
    • P. Prakash Department of Artificial Intelligence and Data Science, Sri Sairam Engineering College, Chennai, India
    • Ashwini Shinde Department of E&TC, Nutan Maharashtra Institute of Technology, Maharashtra, India
    • R G Vidhya Department of ECE, HKBK College of Engineering, Bangalore, India
    https://doi.org/10.14419/kk1qt007

    Received date: July 31, 2025

    Accepted date: September 13, 2025

    Published date: October 4, 2025

  • Cyber threat detection, Backpropagation Neural Network (BPNN), Crested Porcupine Optimization Algorithm (CPOA), Harbor Seal Whiskers Optimiza-tion Algorithm (HSWOA), Tanh-estimator normalization.

  • Abstract

    As the sophistication of cybersecurity threats continues to rise, colleges and universities are increasingly faced with threats to their digital assets. Traditional approaches to forecasting cybersecurity threats tend to fall short in accurately classifying specific threats, processing ultra-high-dimensional data, and responding suitably to patterns of attacks. To address these challenges, this study introduces a novel prediction model: BPNN-CPOA-CSP-UAC. The model incorporates a Backpropagation Neural Network (BPNN), a heavily used model that can learn complexities in data. To address parameter tuning deficiencies, the model employs the Crested Porcupine Optimization Algorithm (CPOA) to discover the best weights and biases for the neural network, significantly enhancing learning performance and predictive accuracy. Further performance improvements are achieved using the Harbor Seal Whiskers Optimization Algorithm (HSWOA) for feature selection. This eliminates duplicate information by choosing the most descriptive features and eliminating irrelevant information, thus improving the model's accuracy. Tanh-estimator normalization is also used during pre-processing to scale input features and thus ensure high-quality data during training. The model was trained and tested using two benchmark intrusion detection datasets, CIC-IDS-2017 and UNSW-NB15. Performance on testing is remarkable with precision and accuracy both at 99.99%, recall, and F1-score at 99.98%. The model also yielded a minimum false positive ratio of 0.0175%, a false negative ratio of 0.0165%, and a quick execution time of 90.5 milliseconds. These results suggest BPNN-CPOA-CSP-UAC as a practical and efficient academic institution cybersecurity threat prediction model. Since it can predict threats quickly and effectively, it is a strong defense tool against emerging cyber threats in educational institutions.

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    Hebri , D. ., Devi, S. . ., Selvam, P. . ., Rao , V. V. G. ., Kattoor, R. A. . ., Sridharan , M. ., Selvalakshmi , B. ., Chokkalingam , A. ., Prakash , P. ., Shinde , A. ., & Vidhya , R. G. . (2025). A Cybersecurity Threat Detection Using Advanced Neural Network Methodologies. International Journal of Basic and Applied Sciences, 14(6), 44-50. https://doi.org/10.14419/kk1qt007