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 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/. Creator An entity primarily responsible for making the resource Wagh, Rupali Sunil; Dokaniya, Tina Title A name given to the resource Early Detection of Cyber Threats in EVCS Using Machine Learning: A Focus on Reconnaissance Attacks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1612 LNNS;pp.143-154 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-2872-1_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-2872-1_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028287055?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028287055?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 Wagh R.S., CHRIST (Deemed to be University), Bengaluru, India; Dokaniya T., CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource There is a significant rise in electric vehicle adoption and robust and secure electric vehicle charging station infrastructure to meet this increasing demand. However, advanced technology is vulnerable to several cyber threats. Primarily starting with reconnaissance attacks, attackers gather information about the system to plan greater attacks. This can further lead to several kinds of attacks such as Denial of Service and Host Attacks where the attacker can bypass firewalls, create false traffic and disrupt service for the users. Thus, it is important to detect and prevent these attacks at an early stage. This paper presents a robust machine learning model in order to detect reconnaissance attacks. To the best of our knowledge, there have not been enough studies that focus on specific attack categories for early detection of cyber threats. The ensemble model used in the study demonstrates an impressive accuracy of 97.71% with a good balance between precision and recall. Moreover, variables related to power consumption which are harder to manipulate are used as features. This approach contributes towards more secure EVCS, fosters user trust and promotes adoption of electric vehicles at large. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Cyber security; Early detection; Machine learning; Reconnaissance attacks Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981952871-4; Language A language of the resource English Type The nature or genre of the resource Conference paper 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