{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T07:13:13Z","timestamp":1781853193867,"version":"3.54.5"},"reference-count":55,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"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":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The rapid growth of the internet of things (IoT) has increased exposure to advanced cyberattacks. However, most existing intrusion detection systems (IDS) rely on outdated or synthetic datasets that do not reflect real deployment conditions. The recently released IDSIoT2024 dataset provides long\u2010term traffic traces from real IoT devices, allowing a more realistic evaluation of intrusion detection models. In this paper, we propose TIDE\u2010Net, a two\u2010stage temporal deep learning framework designed for the characteristics of IDSIoT2024. In Stage 1, the framework performs binary classification to separate benign and malicious traffic, whereas Stage 2 deals with the malicious traffic which is further classified into either three coarse\u2010grained attack categories or twelve fine\u2010grained attack types. Deep neural networks (DNN), one\u2010dimensional convolutional neural networks (CNN), and bidirectional long short\u2010term memory networks (BiLSTM) are evaluated under three settings: Binary, 3\u2010class, and 12\u2010class classification. Among these models, BiLSTM shows the most stable performance across all tasks. It achieves 99.24% accuracy in binary detection, over 98.7% accuracy in 3\u2010class classification, and a macro F1\u2010score of 0.914 in 12\u2010class classification. The proposed two\u2010stage BiLSTM\u2010based pipeline achieves approximately 97% end\u2010to\u2010end accuracy. It also handles class imbalance and temporal patterns more effectively than CNN and DNN baselines. These results provide one of the first comprehensive deep learning benchmarks on IDSIoT2024 and confirm the effectiveness of hierarchical, BiLSTM\u2010based temporal models for IoT intrusion detection.<\/jats:p>","DOI":"10.1002\/cpe.70604","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T06:30:41Z","timestamp":1771396241000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TIDE\u2010Net: A Two\u2010Stage Temporal Deep Learning Framework for Multi\u2010Granular IoT Intrusion Detection"],"prefix":"10.1002","volume":"38","author":[{"given":"Mirza","family":"Qais Baig","sequence":"first","affiliation":[{"name":"School of Software Northwestern Polytechnical University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5445-9728","authenticated-orcid":false,"given":"Ali","family":"Turab","sequence":"additional","affiliation":[{"name":"School of Software Northwestern Polytechnical University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhan","family":"Ullah","sequence":"additional","affiliation":[{"name":"Cybersecurity Center Prince Mohammad Bin Fahd University  Khobar Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2021.1004344"},{"key":"e_1_2_11_3_1","first-page":"1757","volume-title":"Proceedings of the 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2025)","author":"Koppula M.","year":"2025"},{"key":"e_1_2_11_4_1","unstructured":"R.Flood \u201cEnhancing Trust in NIDS: From Flawed Benchmarks to Formal Guarantees \u201d2025."},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12083-025-02078-6"},{"key":"e_1_2_11_6_1","volume-title":"Fine\u2010Grained, Content\u2010Agnostic Network Traffic Analysis for Malicious Activity Detection","author":"Feng Y.","year":"2023"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/jsan12040051"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3590966"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3259474"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3532895"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-96303-0"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24134152"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1049\/PBSE027E_ch9"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2023602"},{"key":"e_1_2_11_15_1","article-title":"Enhancing IoT Network Security Through Deep Learning\u2010Powered Intrusion Detection System","volume":"35","author":"Bakhsh S. 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