Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units
In this paper, we address the limitations of existing deep learning (DL) methods for local misbehavior detection (LMBD) in vehicle-to-everything (V2X) communication systems by proposing an approach that combines rule-based and DL-based techniques. Conventional DL-based methods at roadside units (RSU...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10715733/ |
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| author | Seungyoung Park Duksoo Kim Seokwoo Lee |
| author_facet | Seungyoung Park Duksoo Kim Seokwoo Lee |
| author_sort | Seungyoung Park |
| collection | DOAJ |
| description | In this paper, we address the limitations of existing deep learning (DL) methods for local misbehavior detection (LMBD) in vehicle-to-everything (V2X) communication systems by proposing an approach that combines rule-based and DL-based techniques. Conventional DL-based methods at roadside units (RSUs) struggle with forwarding basic safety messages (BSMs) received from every vehicle to centralized locations and preprocessing them, which leads to considerable time delays. To overcome these challenges, our approach leveraged multi-access edge computing (MEC) connected to RSU to decentralize the processing workload, considerably reducing latency and resource consumption. Specifically, we implemented a system where RSUs directly receive and forward BSMs to the MEC server, bypassing traditional deduplication and sorting processes at the centralized server. However, due to the fixed locations of RSUs, they often receive only truncated sequences of BSMs from passing vehicles, which necessitates LMBD on these incomplete datasets. To mitigate the performance degradation of DL-based anomaly detection in truncated sequences, we integrated a rule-based method performed for single or two consecutively received BSMs. Simulation results demonstrated that this combined rule-based pre-screening with DL analysis effectively improves the overall detection performances. |
| format | Article |
| id | doaj-art-af94f964d215409d997ee9949b63176b |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-af94f964d215409d997ee9949b63176b2025-01-24T00:02:55ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01565666810.1109/OJITS.2024.347971610715733Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside UnitsSeungyoung Park0https://orcid.org/0000-0003-1234-3935Duksoo Kim1Seokwoo Lee2Department of Electrical and Electronics Engineering, Kangwon National University, Chuncheon, South KoreaAUTOCRYPT Company Ltd., Seoul, South KoreaAUTOCRYPT Company Ltd., Seoul, South KoreaIn this paper, we address the limitations of existing deep learning (DL) methods for local misbehavior detection (LMBD) in vehicle-to-everything (V2X) communication systems by proposing an approach that combines rule-based and DL-based techniques. Conventional DL-based methods at roadside units (RSUs) struggle with forwarding basic safety messages (BSMs) received from every vehicle to centralized locations and preprocessing them, which leads to considerable time delays. To overcome these challenges, our approach leveraged multi-access edge computing (MEC) connected to RSU to decentralize the processing workload, considerably reducing latency and resource consumption. Specifically, we implemented a system where RSUs directly receive and forward BSMs to the MEC server, bypassing traditional deduplication and sorting processes at the centralized server. However, due to the fixed locations of RSUs, they often receive only truncated sequences of BSMs from passing vehicles, which necessitates LMBD on these incomplete datasets. To mitigate the performance degradation of DL-based anomaly detection in truncated sequences, we integrated a rule-based method performed for single or two consecutively received BSMs. Simulation results demonstrated that this combined rule-based pre-screening with DL analysis effectively improves the overall detection performances.https://ieeexplore.ieee.org/document/10715733/Vehicle-to-everything (V2X)misbehavior detection (MBD)roadside unit (RSU)misbehavior report (MBR)multi-access edge computing (MEC)deep learning |
| spellingShingle | Seungyoung Park Duksoo Kim Seokwoo Lee Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units IEEE Open Journal of Intelligent Transportation Systems Vehicle-to-everything (V2X) misbehavior detection (MBD) roadside unit (RSU) misbehavior report (MBR) multi-access edge computing (MEC) deep learning |
| title | Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units |
| title_full | Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units |
| title_fullStr | Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units |
| title_full_unstemmed | Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units |
| title_short | Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units |
| title_sort | enhancing v2x security through combined rule based and dl based local misbehavior detection in roadside units |
| topic | Vehicle-to-everything (V2X) misbehavior detection (MBD) roadside unit (RSU) misbehavior report (MBR) multi-access edge computing (MEC) deep learning |
| url | https://ieeexplore.ieee.org/document/10715733/ |
| work_keys_str_mv | AT seungyoungpark enhancingv2xsecuritythroughcombinedrulebasedanddlbasedlocalmisbehaviordetectioninroadsideunits AT duksookim enhancingv2xsecuritythroughcombinedrulebasedanddlbasedlocalmisbehaviordetectioninroadsideunits AT seokwoolee enhancingv2xsecuritythroughcombinedrulebasedanddlbasedlocalmisbehaviordetectioninroadsideunits |