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 Articles Article Faculty Publications -Articles 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 A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection Subject The topic of the resource federated learning; Gated Recurrent Neural Networks; Internet of things; Long Short Term Memory (LSTM) Description An account of the resource The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved. Creator An entity primarily responsible for making the resource Jyotsna A.; Anita M.E.A. Source A related resource from which the described resource is derived International Journal of Advanced Computer Science and Applications, Vol-14, No. 12, pp. 298-310. Publisher An entity responsible for making the resource available Science and Information Organization Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14569/IJACSA.2023.0141231" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2023.0141231</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184655403&amp;doi=10.14569%2FIJACSA.2023.0141231&amp;partnerID=40&amp;md5=a90f4b74da3d7a955ef5084328602681" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184655403&amp;doi=10.14569%2fIJACSA.2023.0141231&amp;partnerID=40&amp;md5=a90f4b74da3d7a955ef5084328602681</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 2158107X 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jyotsna A., Dept. of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India, Dept. of Computer Science and Engineering, RSET, Kochi, India; Anita M.E.A., Dept. of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India, Dept. of Computer Science and Engineering, RSET, Kochi, India