{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:00:35Z","timestamp":1783728035920,"version":"3.55.0"},"reference-count":41,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":350,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>The rapid proliferation of Internet of Things (IoT) infrastructures has introduced significant security challenges due to device heterogeneity, dynamic interactions, and resource limitations. Traditional intrusion detection systems (IDSs) often struggle to capture temporal dependencies and emergent behaviors inherent in modern IoT cyber threats. This study presents a novel hybrid framework that combines deep recurrent neural networks (RNNs), specifically long short\u2010term memory (LSTM) architectures, with complex network modeling to enhance the detection and classification of sophisticated attacks. The proposed system leverages normalized and labeled IoT traffic data, encompassing multiple attack classes (e.g., DoS, DDoS, Brute Force, MITM, and Replay) to train an LSTM\u2010based IDS capable of multiclass temporal analysis. Simultaneously, an IoT network environment is simulated using graph\u2010theoretic principles, where each node represents a device characterized by parameters such as latency, energy usage, and communication protocols. Cyberattack scenarios are emulated within this network to facilitate real\u2010time detection of anomalous behaviors. Experimental results demonstrate the effectiveness of the proposed model in capturing sequential patterns and improving detection accuracy in complex IoT environments.<\/jats:p>","DOI":"10.1155\/cplx\/9693472","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T12:35:33Z","timestamp":1765974933000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intrusion Detection in IoT Using Deep Recurrent Neural Networks: A Complex Network Approach to Modeling Emergent Cyberattack Behaviors"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5228-1857","authenticated-orcid":false,"given":"Roya","family":"Morshedi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3800-8396","authenticated-orcid":false,"given":"S.Mojtaba","family":"Matinkhah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107876"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43926-025-00177-7"},{"key":"e_1_2_11_3_2","first-page":"609","article-title":"Exploring Impact of Data Noise on Iot Security: A Study Using Decision Tree Classification in Intrusion Detection Systems","volume":"11","author":"Matinkhah S. 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