Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs
<p dir="ltr">Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the env...
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2022
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| _version_ | 1864513531623243776 |
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| author | Shamim Younas (17541936) |
| author2 | Faisal Rehman (5664848) Tahir Maqsood (17541939) Saad Mustafa (9421664) Adnan Akhunzada (3134064) Abdullah Gani (3134076) |
| author2_role | author author author author author |
| author_facet | Shamim Younas (17541936) Faisal Rehman (5664848) Tahir Maqsood (17541939) Saad Mustafa (9421664) Adnan Akhunzada (3134064) Abdullah Gani (3134076) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shamim Younas (17541936) Faisal Rehman (5664848) Tahir Maqsood (17541939) Saad Mustafa (9421664) Adnan Akhunzada (3134064) Abdullah Gani (3134076) |
| dc.date.none.fl_str_mv | 2022-12-05T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/app122312448 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Collaborative_Detection_of_Black_Hole_and_Gray_Hole_Attacks_for_Secure_Data_Communication_in_VANETs/24717468 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Automotive engineering Control engineering, mechatronics and robotics Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software vehicle ad hoc networks (VANETs) black hole Gray Hole linear regression (LR) linear discriminant analysis (LDA) support vector machine (SVM) naïve Bayes (NB) |
| dc.title.none.fl_str_mv | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Sciences<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/app122312448" target="_blank">https://dx.doi.org/10.3390/app122312448</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_86c8b4c84b5505a63194bcc0f46de304 |
| identifier_str_mv | 10.3390/app122312448 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717468 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETsShamim Younas (17541936)Faisal Rehman (5664848)Tahir Maqsood (17541939)Saad Mustafa (9421664)Adnan Akhunzada (3134064)Abdullah Gani (3134076)EngineeringAutomotive engineeringControl engineering, mechatronics and roboticsInformation and computing sciencesCybersecurity and privacyData management and data scienceDistributed computing and systems softwarevehicle ad hoc networks (VANETs)black holeGray Holelinear regression (LR)linear discriminant analysis (LDA)support vector machine (SVM)naïve Bayes (NB)<p dir="ltr">Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Sciences<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/app122312448" target="_blank">https://dx.doi.org/10.3390/app122312448</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2022-12-05T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/app122312448https://figshare.com/articles/journal_contribution/Collaborative_Detection_of_Black_Hole_and_Gray_Hole_Attacks_for_Secure_Data_Communication_in_VANETs/24717468CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247174682022-12-05T03:00:00Z |
| spellingShingle | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs Shamim Younas (17541936) Engineering Automotive engineering Control engineering, mechatronics and robotics Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software vehicle ad hoc networks (VANETs) black hole Gray Hole linear regression (LR) linear discriminant analysis (LDA) support vector machine (SVM) naïve Bayes (NB) |
| status_str | publishedVersion |
| title | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| title_full | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| title_fullStr | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| title_full_unstemmed | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| title_short | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| title_sort | Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs |
| topic | Engineering Automotive engineering Control engineering, mechatronics and robotics Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software vehicle ad hoc networks (VANETs) black hole Gray Hole linear regression (LR) linear discriminant analysis (LDA) support vector machine (SVM) naïve Bayes (NB) |