{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T02:06:27Z","timestamp":1777860387942,"version":"3.51.4"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder\u2019s latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity with more than a 99% of precision using only 2 learned features.<\/jats:p>","DOI":"10.1093\/jigpal\/jzae104","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T03:20:52Z","timestamp":1723692052000},"source":"Crossref","is-referenced-by-count":0,"title":["Influence of autoencoder latent space on classifying IoT CoAP attacks"],"prefix":"10.1093","volume":"33","author":[{"given":"Mar\u00eda Teresa","family":"Garc\u00eda-Ord\u00e1s","sequence":"first","affiliation":[{"name":"Department of Electrical and Systems Engineering , University of Le\u00f3n, Escuela de Ingenier\u00edas, Campus de Vegazana, 24071 Le\u00f3n, Spain, mgaro@unileon.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose","family":"Aveleira-Mata","sequence":"additional","affiliation":[{"name":"Research Institute of Applied Sciences in Cybersecurity (RIASC) MIC , University of Le\u00f3n, 24071 Le\u00f3n, Spain, jose.aveleira@unileon.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isa\u00edas","family":"Garc\u00eda-Rodr\u00edgez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Systems Engineering , University of Le\u00f3n, Escuela de Ingenier\u00edas, Campus de Vegazana, 24071 Le\u00f3n, Spain, igarr@unileon.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Luis Casteleiro-Roca","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering , University of A Coru\u00f1a, CTC, CITIC Avda. 19 de febrero s\/n, 15405, Ferrol, A Coru\u00f1a, Spain, jose.luis.casteleiro@udc.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mart\u00edn","family":"Bay\u00f3n-Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Systems Engineering , University of Le\u00f3n, Escuela de Ingenier\u00edas, Campus de Vegazana, 24071 Le\u00f3n, Spain, martin.bayon@unileon.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00e9ctor","family":"Alaiz-Moret\u00f3n","sequence":"additional","affiliation":[{"name":"Research Institute of Applied Sciences in Cybersecurity (RIASC) MIC , University of Le\u00f3n, 24071 Le\u00f3n, Spain, hector.moreton@unileon.es"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"2026042920464936700_ref1","doi-asserted-by":"crossref","first-page":"100656","DOI":"10.1016\/j.iot.2022.100656","article-title":"Deep learning-enabled anomaly detection for iot 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