Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications
<p dir="ltr">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 provisioni...
محفوظ في:
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2020
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513554935185408 |
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| author | Sonain Jamil (20278065) |
| author2 | Fawad (20278068) MuhibUr Rahman (18174361) Amin Ullah (12015113) Salman Badnava (16864356) Masoud Forsat (14158980) Seyed Sajad Mirjavadi (20278071) |
| author2_role | author author author author author author |
| author_facet | Sonain Jamil (20278065) Fawad (20278068) MuhibUr Rahman (18174361) Amin Ullah (12015113) Salman Badnava (16864356) Masoud Forsat (14158980) Seyed Sajad Mirjavadi (20278071) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sonain Jamil (20278065) Fawad (20278068) MuhibUr Rahman (18174361) Amin Ullah (12015113) Salman Badnava (16864356) Masoud Forsat (14158980) Seyed Sajad Mirjavadi (20278071) |
| dc.date.none.fl_str_mv | 2020-07-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s20143923 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Malicious_UAV_Detection_Using_Integrated_Audio_and_Visual_Features_for_Public_Safety_Applications/27824511 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Aerospace engineering Communications engineering AlexNet feature extraction localization public safety malicious drones surveillance |
| dc.title.none.fl_str_mv | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s20143923" target="_blank">https://dx.doi.org/10.3390/s20143923</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_cfb90ef3db9efbf649dcc73767ba9c5f |
| identifier_str_mv | 10.3390/s20143923 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27824511 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety ApplicationsSonain Jamil (20278065)Fawad (20278068)MuhibUr Rahman (18174361)Amin Ullah (12015113)Salman Badnava (16864356)Masoud Forsat (14158980)Seyed Sajad Mirjavadi (20278071)EngineeringAerospace engineeringCommunications engineeringAlexNetfeature extractionlocalizationpublic safetymalicious dronessurveillance<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s20143923" target="_blank">https://dx.doi.org/10.3390/s20143923</a></p>2020-07-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s20143923https://figshare.com/articles/journal_contribution/Malicious_UAV_Detection_Using_Integrated_Audio_and_Visual_Features_for_Public_Safety_Applications/27824511CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/278245112020-07-15T03:00:00Z |
| spellingShingle | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications Sonain Jamil (20278065) Engineering Aerospace engineering Communications engineering AlexNet feature extraction localization public safety malicious drones surveillance |
| status_str | publishedVersion |
| title | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| title_full | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| title_fullStr | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| title_full_unstemmed | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| title_short | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| title_sort | Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications |
| topic | Engineering Aerospace engineering Communications engineering AlexNet feature extraction localization public safety malicious drones surveillance |