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Push-Based Content Dissemination and Machine Learning-Oriented illusion attack Detection in Vehicular Named Data Networking

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Computers, Materials & Continua 2023年第9期76卷 3131-3150页

作者： Arif Hussain Magsi Ghulam Muhammad Sajida Karim Saifullah Memon Zulfiqar Ali State Key Laboratory ofNetworking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related ***,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding ***,the NDN communication framework fails to address two important *** current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous *** various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to *** deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding *** addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or *** trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack *** results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type ***,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high *** ultimate goal of our research is to improve content exchange and ***,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

关键词： Named data networking vehicular networks pull-push illusion attack machine learning

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