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 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/. Creator An entity primarily responsible for making the resource Paul, Aditi; Sinha, Somnath; Mishra, Saumya Title A name given to the resource A Hybrid Intrusion Detection System for detecting Cross-layer DoS attacks in 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 Wireless Personal Communications;Volume;145;Issue;2026-04-03 00:00:00;pp.717-740 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11277-025-11787-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11277-025-11787-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010259219?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010259219?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., Department of Computer Science, Banasthali Vidyapith, Rajasthan, Banasthali, 304022, India; Sinha S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Mishra S., Department of Computer Science, Banasthali Vidyapith, Rajasthan, Banasthali, 304022, India Description An account of the resource The Internet of Things (IoT) is critically prone to Denial of Service (DoS) attacks at multiple layers. If designed carefully, intrusion detection systems (IDS) can detect these attacks effectively. In the proposed study, we develop a Hybrid IDS to detect Cross-Layer DoS attacks in IoT. The proposed Cross-Layer system reduces the false positive rate considerably than a single IDS. The IDS is designed by ensembling multiple machine learning techniques to avoid overfitting or underfitting. The Hybrid IDS works in two stages, the first stage for detection of the attack occurrence (Anomaly detection) followed by a second stage to classify the attack types (Signature of the attacks). The output of the first stage is Correctly Detected Samples (CDS), which are again tested by the second stage to get Correctly Classified Samples (CCS). Another unique aspect of the proposed study is the dataset generation for different attacks considered. Rather than using the existing dataset, we have developed a trace file in NetSim Simulator by designing an attack environment. At the same time, during the feature selection process, a novel and efficient technique is applied to select the best feature set along with the critical component (CF). Simulation results accurately detect CDS of up to 95% and CCS of up to 96% with a weighted average F1 score. The testing time of the proposed model is also considerably lower than that of individual models, which makes the system efficient and lightweight. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. Subject The topic of the resource Cross-layer IDS; DoS attack; Internet of Things; Machine learning-based IDS; Stacking ensemble Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 9296212; CODEN: WPCOF Language A language of the resource English Type The nature or genre of the resource Article 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