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 Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep; Joseph, P. Mani Title A name given to the resource ONLINE CUSTOMERS PURCHASE INTENTIONS CLASSIFICATION USING HYBRID XGBOOST MACHINE LEARNING MODEL AND ITS SECURITY CONCERNS 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 Attacks on Artificial Intelligence: The New Facets of Cyber Ecospace;pp.137-154 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105018271559?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018271559?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 Prathap M.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University, Bangalore, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University, Bangalore, India; Joseph P.M., Department of Computer Science, Modern College of Business and Science, Muscat, Oman Description An account of the resource With technology advancing rapidly and data growing at an unprecedented rate, e-commerce businesses are gathering extensive customer informa tion. Yet, many organizations struggle to fully leverage this data. Grasping customer behaviour is vital for businesses aiming to create effective marketing strategies, boost product visibility, and drive sales. Several factors affect consumer purchasing decisions, including search history, time spent viewing products, bounce rates, and demographic details like age, gender, and income. This study investigates the key elements influencing customer behaviour and investigates data mining techniques to enhance revenue predictions. A major challenge in e-commerce is converting website traffic into actual sales. Attracting visitors alone does not ensure revenue. To tackle this issue, classification techniques are used to assess the likelihood of a purchase based on user interactions. Furthermore, while artificial intel ligence and machine learning have revolutionized e-commerce analytics, security issues persist. Adversarial attacks on machine learning models can create risks, making security a critical consideration. This research offers a thorough analysis of classification techniques for evaluating business revenue generation. It also examines ways to improve model accuracy while addressing security weaknesses. The findings aim to enhance e-commerce decision-making, customer targeting, and overall business growth. 2026 by Apple Academic Press, Inc. Publisher An entity responsible for making the resource available Apple Academic Press Relation A related resource ISBN: 978-104009235-4; 978-177491622-3; 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