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@article{168400,
        author = {S.Antony Lishma and M.Deepa Dharshini and L.sajila},
        title = {A Study on Real-Time Adaptation of AI Models to Emerging Cyber Threats},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1269-1272},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168400},
        abstract = {In an increasingly digital world, cyber threats are evolving at an unprecedented pace, necessitating the development of adaptive AI models capable of responding in real-time. This study explores the intricacies of real-time adaptation in AI models to counter emerging cyber threats, highlighting recent advancements, methodologies, and case studies. By examining various strategies employed in this domain, we aim to provide a comprehensive understanding of how AI can be leveraged to enhance cybersecurity measures. The paper concludes with recommendations for future research and practical applications.},
        keywords = {AI adaptation, real-time cybersecurity, emerging cyber threats, machine learning, deep learning, anomaly detection, threat intelligence, adaptive algorithms, automated response.},
        month = {October},
        }