Abstract
Abstract: Robust and efficient data security measures are of the utmost importance given the growing reliance on digital technologies. I recommend developing a simulated security testing platform using artificial immune algorithms to improve data security in response to the needs of the user. This software provides the capacity to simulate different cyber-attacks, making it easier to assess the efficacy of different safeguards. The platform may adapt to these attacks and learn from them by utilizing the strength of artificial immune mechanisms, increasing its resistance to threats in the future. I acquired beneficial expertise in software development, algorithm design, and data security as a result of my active participation in this project. I'm thrilled to share this study as a proof of my abilities and unwavering commitment to the data security area. The suggested approach makes use of Python-based machine learning techniques, and adds HTML, CSS, and JS for the user interface. The suggested approach also includes a user-friendly interface built using HTML, CSS, and JS, making it easier to integrate with present systems.
Published Version
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