{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T06:30:51Z","timestamp":1723789851714},"reference-count":50,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T00:00:00Z","timestamp":1594771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.<\/jats:p>","DOI":"10.3390\/s20143923","type":"journal-article","created":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T14:54:46Z","timestamp":1594911286000},"page":"3923","source":"Crossref","is-referenced-by-count":47,"title":["Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7139-7389","authenticated-orcid":false,"given":"Sonain","family":"Jamil","sequence":"first","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology, Taxila, Punjab 47050, Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3860-2635","authenticated-orcid":false,"family":"Fawad","sequence":"additional","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology, Taxila, Punjab 47050, Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5084-7862","authenticated-orcid":false,"given":"MuhibUr","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada"}]},{"given":"Amin","family":"Ullah","sequence":"additional","affiliation":[{"name":"College of Engineering & Computer Science (CECS), Center for Research in Computer Vision Lab (CRCV Lab), University of Central Florida (UCF), Orlando, FL 32816, USA"}]},{"given":"Salman","family":"Badnava","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Qatar University, P.O. Box Doha 2713, Qatar"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8563-1324","authenticated-orcid":false,"given":"Masoud","family":"Forsat","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box Doha 2713, Qatar"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8833-8273","authenticated-orcid":false,"given":"Seyed Sajad","family":"Mirjavadi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box Doha 2713, Qatar"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rice, A.B. (2016, January 13\u201320). Drone technology as applied to the cement industry. 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