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 Sethurajan, Monikka Reshmi; Natarajan, K. Title A name given to the resource FusionBotSentinel: A Framework to Mitigate Probable Social Bots Spreading False Information in Cyber Physical Systems 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 Lecture Notes in Networks and Systems;Volume;1277 LNNS;pp.353-371 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2700-4_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2700-4_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011271074?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011271074?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 Sethurajan M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Natarajan K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India Description An account of the resource The escalating dissemination of fake news across social media networks has emerged as a concerning societal issue and a threat to cyber physical systems. Bots, often employed to propagate such misinformation, present a formidable challenge in their detection and elimination. Bot prediction have been pivotal in identifying and curbing these deceptive bot activities within social media networks. Twitchs live streaming content is readily scrapable and totally accessible. But quite understudied. Recent studies scrutinized these frameworks, revealing significant strides in their development while acknowledging the need for further enhancements in both predictions for proactive measures. FusionBotSentinel proposes a novel architecture that underscores the imperative for future research to concentrate on fortifying these frameworks, ensuring they are more resilient and adaptable in mitigating and predicting the spread of fake news by social bots. Another focus is on enhancing the effectiveness of deep learning models through a refined understanding of data quality with a largest dataset available and employing better hybrid techniques that bolster the generalizability and robustness helping in forecasting bot activities in combatting this escalating problem within cyber physical systems. Since bots are seen to be the source of the present problems with cyber physical systems, including privacy, security, safety, and ethical difficulties, it is necessary to recognize these gaps. Our suggested FusionBotSentinelprovides a revolutionary significance by contributing to in combatting fake news in the society by achieving up to 99% in accuracy, 98% in precision, 100% in recall, 99% in sensitivity with F1 score as 99% in social bot prediction offering 20% more efficiency when compared to the most advanced existing models proving its superiority. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cyber physical systems; Deep learning; Fake news; Social bots; Social media network; Twitch Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962699-1; 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