Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks

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dc.authoridPolat, Huseyin/0000-0003-4128-2625
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.contributor.authorPolat, Onur
dc.contributor.authorOyucu, Saadin
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorPolat, Hueseyin
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorYardimci, Fahri
dc.date.accessioned2025-05-04T16:45:49Z
dc.date.available2025-05-04T16:45:49Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractVehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture.
dc.description.sponsorshipEuropean Union's Horizon Europe Research and Innovation Programme; [101095863]
dc.description.sponsorshipThis paper is supported by the European Union's Horizon Europe Research and Innovation Programme under grant agreement No. 101095863, project FLEXSHIP.
dc.identifier.doi10.3390/app142210501
dc.identifier.issn2076-3417
dc.identifier.issue22
dc.identifier.scopus2-s2.0-85210443106
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app142210501
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35213
dc.identifier.volume14
dc.identifier.wosWOS:001366909600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectSDN
dc.subjectDDoS attack
dc.subjectVANET
dc.subjectdeep learning
dc.subjectreal-time intrusion detection
dc.subjectintelligent transportation systems
dc.titleHybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks
dc.typeArticle

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