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Publications

13 of 24 publications

  • PublishedBook chapter

    Application of Artificial Intelligence for DDoS Attack Detection and Prevention on Cyber Physical Systems Using Deep Learning

    Citation: Akinsola, J. E. T., Abimbola, R. O., Adeagbo, M. A., Awoseyi, A. A., Onipede, F. O., & Yusuf, A. A. (2022). Application of Artificial Intelligence for DDoS Attack Detection and Prevention on Cyber Physical Systems Using Deep Learning. In Internet of Things and Cyber Physical Systems (CRC Press) (pp. 83–126). https://doi.org/10.1201/9781003283003-5

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  • PublishedBook chapter

    Hybridization of Decision Tree Algorithm Using Sequencing Predictive Model for COVID-19

    Citation: Awoseyi, A. A., Akinsola, J. E. T., Oladoja, O. M., Adeagbo, M., & Adebowale, O. O. (2022). Hybridization of Decision Tree Algorithm Using Sequencing Predictive Model for COVID-19. In Emerging Technologies for Combatting Pandemics (CRC Press) (pp. 201–221). https://doi.org/10.1201/9781003324447-10

    DOI for Hybridization of Decision Tree Algorithm Using Sequencing Predictive Model for COVID-19 (opens in a new tab)
  • PublishedBook chapter

    Intelligent Optimization and Computational Learning Techniques for Mitigating Pandemics

    Citation: Oladapo, K. A., Akinsola, J. E. T., Adeagbo, M., Onipede, F., Akinseinde, S. A., & Yusuf, A. A. (2022). Intelligent Optimization and Computational Learning Techniques for Mitigating Pandemics. In Emerging Technologies for Combatting Pandemics (CRC Press) (pp. 143–173). https://doi.org/10.1201/9781003324447-8

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  • PublishedBook chapter

    Artificial Intelligence Emergence in Disruptive Technology

    Citation: Akinsola, J. E. T., Adeagbo, M. A., Oladapo, K. A., Akinsehinde, S. A., & Onipede, F. O. (2022). Artificial Intelligence Emergence in Disruptive Technology. In Computational Intelligence and Data Sciences: Paradigms in Biomedical Engineering (CRC Press) (pp. 63–90). https://doi.org/10.1201/9781003224068-4

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  • PublishedBook chapter

    Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

    Citation: Akinsola, J. E. T., Akinseinde, S., Kalesanwo, O., Adeagbo, M., Oladapo, K., Awoseyi, A., & Kasali, F. (2021). Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling. In Software Usability (IntechOpen). https://doi.org/10.5772/intechopen.96534

    Show abstract

    In recent years, Cyber Security threat modeling has been discovered to have the capacity of combatting and mitigating against online threats. In order to minimize the associated risk, these threats need to be modelled with appropriate Intelligent User Interface (IUI) design and consequently the development and evaluation of threat metrics. Artificial Intelligence (AI) has revolutionized every facet of our daily lives and building a responsive Cyber Security Threat Model requires an IUI. The current threat models lack IUI, hence they cannot deliver convenience and efficiency. However, as the User Interface (UI) functionalities and User Experience (UX) continue to increase and deliver more astonishing possibilities, the present threat models lack the predictability capacity thus Machine Learning paradigms must be incorporated. Meanwhile, this deficiency can only be handled through AI-enabled UI that utilizes baseline principles in the design of interfaces for effective Human-Machine Interaction (HMI) with lasting UX. IUI helps developers or designers enhance flexibility, usability, and the relevance of the interaction to improving communication between computer and human. Baseline principles must be applied for developing threat models that will ensure fascinating UI-UX. Application of AI in UI design for Cyber Security Threat Modeling brings about reduction in critical design time and ensures the development of better threat modeling applications and solutions.

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