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@article{173513,
        author = {C.Indra Joshna and Dr.M.Arathi},
        title = {SECURE E-LEARNING USING DATA MINING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {575-581},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173513},
        abstract = {This study explores the integration of data mining techniques to ensure the security of online learning environments. In the rapidly expanding field of e-learning, the application of data mining has proven to be an essential tool for managing vast amounts of educational data. However, ensuring the secure use of these techniques remains a significant challenge. Data mining plays a crucial role in identifying patterns and predicting behaviour in online educational platforms, but its application must be approached with security in mind. The study proposes methods for applying data mining securely in e-learning systems, focusing on maintaining the privacy of student data, ensuring content security, and enhancing the overall quality of learning. Tools such as elliptic curve encryption are employed to protect sensitive information, while algorithms like decision trees and random forests help filter and optimize content delivery. With a growing reliance on e-learning platforms, this research emphasizes the need for securing both the student experience and the intellectual property of course content. While there are challenges in addressing security concerns, the proposed approach can offer significant benefits to students, educators, and providers. The aim is to demonstrate that secure data mining not only facilitates efficient learning but also guarantees the safety of all involved parties.},
        keywords = {Data Mining, E-Learning Security, Elliptic Curve Encryption, Decision Tree Algorithm, Random Forest Algorithm.},
        month = {March},
        }