Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singhal, Divya; Verma, Ankit; Radhakrishnan, Ganesh V.; Parashar, Jyoti; Date, Saroj S.; Upreti, Kamal Title A name given to the resource Whale Optimization and AutoML for Precise Phishing Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Mobile Multimedia;Volume;21;Issue;5;pp.855-880 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13052/jmm1550-4646.2153" target="_blank" rel="noreferrer noopener">https://doi.org/10.13052/jmm1550-4646.2153</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020064783?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020064783?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singhal D., Department of Computer Science, Noida Institute of Engineering &amp; Technology, Greater Noida, India; Verma A., Department of Computer Applications, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India; Radhakrishnan G.V., Department of Economics and Finance, KIIT School of Management (KSOM), KIIT University, Bhubaneswar, India; Parashar J., Department of Computer Applications, Panipat Institute of Engineering &amp; Technology College, Haryana, Panipat, India; Date S.S., Department of Artificial Intelligence and Data Science, Chh.Shahu College of Engineering, Kanchanwadi, Paithan Road, Chhatrapati Sambhajinagar, MS, Aurangabad, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India Description An account of the resource Online fraud and social engineering tactics frequently use phishing websites as platforms. Phishers often modify the source code of the web pages they exploit in their attacks to create the illusion that alterations were made to authentic websites. A solitary response is insufficient to mitigate phishing due to the many methods employed in its execution. This study examines machine learning algorithms and evaluates their efficacy when trained on datasets including attributes that differentiate secure websites from phishing sites. Automated algorithms facilitate real-time fraud protection by swiftly detecting suspicious URLs, domain names, and website content. This study aims to identify the optimal method for detecting a prevalent category of cyberattacks. This would enhance the security and privacy of all internet users by facilitating the identification and blocking of malicious websites. Nonetheless, there is an urgent desire for automated models that provide rapid and precise detection. This research introduces a regression-based assessment method for phishing detection to address this demand. Our approach employs a whale optimization algorithm for feature selection. An AutoML framework subsequently utilizes the selected feature subsets as input. The model showed good accuracy in its predictions with very small errors on the test data, shown by an RMSE of 0.1079, an MSE of 0.0116, and an R2 value of 0.9534. These results demonstrate the reliability of our feature selection and modeling methods. 2025 River Publishers Subject The topic of the resource AutoML framework; AutoML H2O; optimization algorithm; Phishing attack; random forest algorithm; regression analysis; whale optimization algorithm Publisher An entity responsible for making the resource available River Publishers Relation A related resource ISSN: 15504646; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online