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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Malicious URL Detection Using Machine Learning Techniques Subject The topic of the resource AdaBoost; Binary features; Count features; Cyber security; Lexical features; Machine learning; Malicious URL detection; Random forest; Voting classifier Description An account of the resource Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Angadi S.; Shukla S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-458, pp. 657-669. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-2894-9_50" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-2894-9_50</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137054771&amp;doi=10.1007%2F978-981-19-2894-9_50&amp;partnerID=40&amp;md5=17557aed1b124e177885acf2eb679e15" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137054771&amp;doi=10.1007%2f978-981-19-2894-9_50&amp;partnerID=40&amp;md5=17557aed1b124e177885acf2eb679e15</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981192893-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Angadi S., Christ University, Bangalore, India; Shukla S., Christ University, Bangalore, India