计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 518-523.doi: 10.11896/jsjkx.200700129

• 信息安全 • 上一篇    下一篇

基于Spark的车联网分布式组合深度学习入侵检测方法

俞建业1, 戚湧1, 王宝茁2   

  1. 1 南京理工大学计算机科学与工程学院 南京210094
    2 江苏中达智能交通产业研究院有限公司 江苏 常州213000
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 戚湧(790815561@qq.com)
  • 作者简介:365633995@qq.com
  • 基金资助:
    国家重点研发计划政府间国际科技创新合作重点专项(2016YFE0108000);工业和信息化部网络安全技术应用试点示范项目:智能网联车路协同通信安全研究应用平台;江苏省重点研发计划(产业前瞻与共性关键技术)项目(BE2017163);江苏省交通运输科技项目(2018Y45)

Distributed Combination Deep Learning Intrusion Detection Method for Internet of Vehicles Based on Spark

YU Jian-ye1, QI Yong1, WANG Bao-zhuo2   

  1. 1 School of Computer Science and Engineering,Nanjing University of Science & Technology,Nanjing 210094,China
    2 Jiangsu Zhongda Intelligent Transportation Industry Research Institute Co.Ltd,Changzhou,Jiangsu 213000,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:YU Jian-ye,born in 1993,postgraduate.His main research interests include information security.
    QI Yong,born in 1970,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include traffic bigdata,security of internet of vehicles.
  • Supported by:
    National Key Research and Development Program Intergovernmental Key Items for International Science and Technology Innovation Cooperation of China(2016YFE0108000),Ministry of Industry and Information Technology Network Security Technology Application Pilot Demonstration Project:Intelligent Networked Vehicle-road Cooperative Communication Safety Research Application Platform,Key Research and Development Program of Jiangsu Province China(BE2017163) and JiangSu Transportation Technology Project(2018Y45).

摘要: 随着5G等技术在车联网领域中被广泛应用,入侵检测作为车联网信息安全重要的检测工具发挥着越来越重要的作用。由于车联网结构变化快,数据流量大,入侵形式复杂多样,传统检测方法无法确保其准确性和实时性要求,不能直接被应用到车联网。针对这些问题,提出了一种基于Apache Spark框架的车联网分布式组合深度学习入侵检测方法,通过构建Spark集群,将深度学习卷积神经网络(Convolutional Neural Networks,CNN)和长短期记忆网络(LSTM)组合,进行车联网入侵特征提取和数据检测,从大规模车联网数据流量中发现异常行为。实验结果证明,与其他现有模型相比,该模型算法在时间上最快达到20.1s,准确率最高可达99.7%,具有较好的检测效果。

关键词: Apache Spark, CNN, LSTM, 车联网, 入侵检测

Abstract: With the application of 5G and other technologies in the field of Internet of vehicles,intrusion detection as an important detection tool for information security of Internet of vehicles plays an increasingly important role.Due to the rapid change of the structure of the Internet of vehicles,large data flow,complex and diverse forms of intrusion,traditional detection unable ensure the accuracy and real-time requirements,and unable be directly applied to the Internet of vehicles.To solve these problems,this paper proposes a distributed combination deep learning intrusion detection method for Internet of vehicles based on Apache spark framework.By constructing spark cluster,the deep learning CNN and LSTM are combined to extract intrusion features and detect data,and find abnormal behaviors from large-scale Internet of vehicles data traffic.Experimental results show that,compared with other existing models,the proposed method can achieve 20.1s in time and 99.7% in accuracy.

Key words: Apache Spark, CNN, Internet of vehicles, Intrusion detection, LSTM

中图分类号: 

  • TP302.7
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