Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles
Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be ro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9241834/ |
| _version_ | 1818431104280952832 |
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| author | Zohaib Hassan Amjad Mehmood Carsten Maple Muhammad Altaf Khan Abdulaziz Aldegheishem |
| author_facet | Zohaib Hassan Amjad Mehmood Carsten Maple Muhammad Altaf Khan Abdulaziz Aldegheishem |
| author_sort | Zohaib Hassan |
| collection | DOAJ |
| description | Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput. |
| first_indexed | 2024-12-14T15:44:00Z |
| format | Article |
| id | doaj.art-038bfe2bbf8a40f08d1a9b62b6a242dd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:44:00Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-038bfe2bbf8a40f08d1a9b62b6a242dd2022-12-21T22:55:32ZengIEEEIEEE Access2169-35362020-01-01819961819962810.1109/ACCESS.2020.30343279241834Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected VehiclesZohaib Hassan0https://orcid.org/0000-0002-8100-5473Amjad Mehmood1https://orcid.org/0000-0003-3941-4617Carsten Maple2https://orcid.org/0000-0002-4715-212XMuhammad Altaf Khan3https://orcid.org/0000-0002-2650-4149Abdulaziz Aldegheishem4https://orcid.org/0000-0003-3287-5357Institute of Computing, Kohat University of Science and Technology, Kohat, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanSecure Cyber Systems Research Group, WMG, University of Warwick, Coventry, U.K.Institute of Computing, Kohat University of Science and Technology, Kohat, PakistanDepartment of Urban Planning, College of Architecture and Planning, Traffic Safety Technologies Chair, King Saud University, Riyadh, Saudi ArabiaDetection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput.https://ieeexplore.ieee.org/document/9241834/ACVsVANETsMANETsdetectionblack holeAODV |
| spellingShingle | Zohaib Hassan Amjad Mehmood Carsten Maple Muhammad Altaf Khan Abdulaziz Aldegheishem Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles IEEE Access ACVs VANETs MANETs detection black hole AODV |
| title | Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles |
| title_full | Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles |
| title_fullStr | Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles |
| title_full_unstemmed | Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles |
| title_short | Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles |
| title_sort | intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles |
| topic | ACVs VANETs MANETs detection black hole AODV |
| url | https://ieeexplore.ieee.org/document/9241834/ |
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