Intelligent Framework for Secure Transportation Systems Using Software-Defined-Internet of Vehicles

作     者：Mohana Priya Pitchai Manikandan Ramachandran Fadi Al-Turjman Leonardo Mostarda 

作者机构：School of ComputingSASTRA Deemed UniversityThanjavur613401India Research Centre for A.I.and IoTNear East UniversityNicosiaMersin10Turkey Department of Software EngineeringCamareno UniversityItaly 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3947-3966页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Internet of things smart cities software-defined network intelligent transportation system fuzzy inference system 

摘      要：The Internet of Things plays a predominant role in automating all real-time *** such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic *** vehicles are connected to the internet through wireless communication technologies,the Internet of Vehicles network infrastructure is susceptible to flooding *** the network infrastructure is difficult as network customization is not *** Software Defined Network provide a flexible programming environment for network customization,detecting flooding attacks on the Internet of Vehicles is integrated on top of *** basic methodology used is crypto-fuzzy rules,in which cryptographic standard is incorporated in the traditional fuzzy *** this research work,an intelligent framework for secure transportation is proposed with the basic ideas of security attacks on the Internet of Vehicles integrated with software-defined *** intelligent framework is proposed to apply for the smart city *** proposed cognitive framework is integrated with traditional fuzzy,cryptofuzzy and Restricted Boltzmann Machine algorithm to detect malicious traffic flows in Software-Defined-Internet of *** is inferred from the result interpretations that an intelligent framework for secure transportation system achieves better attack detection accuracy with less delay and also prevents buffer overflow *** proposed intelligent framework for secure transportation system is not compared with existing methods;instead,it is tested with crypto and machine learning algorithms.