{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:58:46Z","timestamp":1777633126713,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University","award":["RSPD2023R564"],"award-info":[{"award-number":["RSPD2023R564"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model\u2019s effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%).<\/jats:p>","DOI":"10.3390\/s23198077","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T03:45:23Z","timestamp":1695699923000},"page":"8077","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Secure and Privacy-Preserving Intrusion Detection and Prevention in the Internet of Unmanned Aerial Vehicles"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0121-9050","authenticated-orcid":false,"given":"Ernest","family":"Ntizikira","sequence":"first","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian 116024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1810-3019","authenticated-orcid":false,"given":"Wang","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian 116024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-8667","authenticated-orcid":false,"given":"Fahad","family":"Alblehai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0278-6492","authenticated-orcid":false,"given":"Kiran","family":"Saleem","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian 116024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9070-6271","authenticated-orcid":false,"given":"Muhammad Ali","family":"Lodhi","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian 116024, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1109\/TITS.2020.2997832","article-title":"Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution","volume":"22","author":"Ning","year":"2021","journal-title":"IEEE Trans. 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