Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking  

作　　者：Arif Hussain Magsi Ghulam Muhammad Sajida Karim Saifullah Memon Zulfiqar Ali 

机构地区：[1]State Key Laboratory ofNetworking and Switching Technology,Beijing University of Post and Telecommunication,Beijing,100876,China [2]Department of Computer Engineering,College of Computer and Information Sciences,King Saud University,Riyadh,11543,Saudi Arabia [3]School of Computer Science and Technology,Harbin Institute of Technology,Harbin,150001,China [4]School of Computer Science and Electronic Engineering,University of Essex,Colchester,CO43SQ,UK

出　　处：《Computers, Materials & Continua》2023年第9期3131-3150,共20页计算机、材料和连续体（英文）

基　　金：supported by the Researchers Supporting Project Number(RSP2023R34);King Saud University,Riyadh,Saudi Arabia。

摘　　要：Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related challenges.Specifically,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding features.However,the NDN communication framework fails to address two important issues.The 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 vehicles.Although various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to VNDN.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 mechanism.In addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or legitimate.We 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 detection.Our 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 8.Thus,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high accuracy.The ultimate goal of our research is to improve content exchange and secureVNDNfromattackers.Thus,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 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]