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 Book Chapter Faculty Publications- Book Chapter 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 Mathew, Calvin; Poonia, Ramesh Chandra Title A name given to the resource A comparative study of machine learning: Models for web tracker 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 Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow;pp.245-263 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003569251-13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003569251-13</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024615475?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024615475?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 Mathew C., School of Sciences, CHRIST University, Delhi NCR, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University, Bengaluru, India Description An account of the resource Web trackers, used by websites, collect user data and monitor online activity, often with or without explicit consent. With concerns for online privacy, there is a growing need to detect these web trackers. This study evaluates several machine learning (ML) techniques for detecting web trackers, focusing on evaluating their performance from the key metrics such as accuracy, precision, and recall. We analyzed supervised methods, such as random forest, support vector machines (SVM), neural networks, gradient boosting, and unsupervised methods, including DBSCAN and isolation forest. Models were trained on a comprehensive dataset extracted from URLs with feature engineering, and data preprocessing techniques were applied to enhance model performance and detect both known and unknown trackers and normal traffic. Our results indicate that supervised models outperform unsupervised methods, demonstrating their superior ability in distinguishing web trackers from normal traffic. This work highlights the effectiveness of ML-based tracker detection and outlines opportunities for improving privacy protection through adaptive supervised learning methods. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104054278-1; 978-103294165-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online