A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing
Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overh...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-10-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864821000705 |
| _version_ | 1828130305028915200 |
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| author | Anirudh Paranjothi Mohammed Atiquzzaman |
| author_facet | Anirudh Paranjothi Mohammed Atiquzzaman |
| author_sort | Anirudh Paranjothi |
| collection | DOAJ |
| description | Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False–Positive Rate (FPR). We propose herein Greenshield's traffic model–based fog computing scheme called Fog–based Rogue Node Detection (F–RouND), which dynamically utilizes the On–Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities. The performance of the F–RouND framework was evaluated via simulations. Results show that the F–RouND framework ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at the urban scenario than the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40% in the region. |
| first_indexed | 2024-04-11T16:32:07Z |
| format | Article |
| id | doaj.art-f601818286704dc2839f8644a23abecd |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-04-11T16:32:07Z |
| publishDate | 2022-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-f601818286704dc2839f8644a23abecd2022-12-22T04:14:00ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482022-10-0185814824A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computingAnirudh Paranjothi0Mohammed Atiquzzaman1Corresponding author.; School of Computer Science, University of Oklahoma, Norman, OK, USASchool of Computer Science, University of Oklahoma, Norman, OK, USARogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False–Positive Rate (FPR). We propose herein Greenshield's traffic model–based fog computing scheme called Fog–based Rogue Node Detection (F–RouND), which dynamically utilizes the On–Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities. The performance of the F–RouND framework was evaluated via simulations. Results show that the F–RouND framework ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at the urban scenario than the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40% in the region.http://www.sciencedirect.com/science/article/pii/S2352864821000705VANETsRogue nodesFog computingIntrusion detection |
| spellingShingle | Anirudh Paranjothi Mohammed Atiquzzaman A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing Digital Communications and Networks VANETs Rogue nodes Fog computing Intrusion detection |
| title | A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing |
| title_full | A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing |
| title_fullStr | A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing |
| title_full_unstemmed | A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing |
| title_short | A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing |
| title_sort | statistical approach for enhancing security in vanets with efficient rogue node detection using fog computing |
| topic | VANETs Rogue nodes Fog computing Intrusion detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352864821000705 |
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