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 Paul, Aditi; Chaudhary, Shirashti; Sinha, Somnath Title A name given to the resource A Machine Learning-Based Cross-Layer DoS Attack Detection Technique for IoT 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;1073;pp.11-23 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-5703-9_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-5703-9_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000326371?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000326371?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 Paul A., Banasthali Vidyapith, Rajasthan, Tonk, 304022, India; Chaudhary S., Banasthali Vidyapith, Rajasthan, Tonk, 304022, India; Sinha S., CHRIST (Deemed to be University), Bengaluru, 560029, India Description An account of the resource The most potent and common attacks on Internet of Things (IoT) are Denial of Service (DoS) attacks. Unfortunately, because the attack occurs on numerous layers, a single layer detection method is insufficient and ineffective to counteract these attacks. The current work focuses on the detection of cross-layer DoS assaults using Machine Learning-based multiclass classifiers. Three attacks against Routing Protocol (RPL) and Transmission Control Protocol (TCP) are detected using three ML Classifiers (KNN, Gradient Boosting, Random Forest). The novelty of the study is the design of cross-layer attack datasets using feature engineering technique. The performances of the classifiers are analyzed in presence of both balanced and imbalanced datasets. The results show that Gradient Boosting classifier has highest accuracy of up to 98% with deviation of up to 97%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cross-layer DoS attacks; IoT security; ML classifier; RPL attacks; Sinkhole attack Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981975702-2; 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