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@article{167569,
        author = {Puli Raju and Kothapalli Venkata Naga Aditya and Punnapu Karthik},
        title = {Optimized and Relevant Features for Enhanced Phishing Detection Accuracy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1749-1755},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167569},
        abstract = {for effective detection systems grows. This research describes a novel method for improving phishing detection accuracy by optimizing feature selection. By evaluating a large dataset of phishing attempts, we find crucial parameters that boost detection rates while lowering false positives. Our strategy focuses on extracting the best possible common features that causes phishing. We will be  overcoming the concerns by applying modern machine learning methods to assure effective and reliable detection systems, providing a vital tool for cybersecurity practitioners. This work adds to the ongoing efforts to secure online spaces by introducing a well-designed phishing threat management system by providing most relevant features for the phishing attacks},
        keywords = {Phishing, ETL, Binary Classification, Feature Selection},
        month = {September},
        }