Machine Learning-Based Intrusion Detection System for Big Data Analytics in VANET

Mingyuan Zang, Ying Yan

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Attacks as Distributed Denial of Service (DDoS) are ones of the most frequent vehicle cybersecurity threats. In this paper, we propose a Machine Learning-based Intrusion Detection System (IDS) for monitoring network traffic and detecting abnormal activities. This IDS framework integrates streaming engines for big data analytics, management and visualization. A Vehicular ad-hoc network (VANET) topology of multiple connected nodes with mobility capability is simulated in the Mininet-Wifi environment. Real-time data is collected using the sFlow technology and transmitted from the simulator to our proposed IDS framework. We have achieved high detection accuracy results by training the Random Forest as the classifier to label out the anomalous flows. Additionally, the network throughput has been evaluated and compared with and without deploying the proposed IDS. The results verify the system is a lightweight solution by bringing little burden to the network.

Original languageEnglish
Title of host publicationProceedings of IEEE 93rd Vehicular Technology Conference
Publication dateApr 2021
Article number9448878
ISBN (Electronic)9781728189642
Publication statusPublished - Apr 2021
Event93rd IEEE Vehicular Technology Conference - Virtual event, Helsinki, Finland
Duration: 25 Apr 202128 Apr 2021


Conference93rd IEEE Vehicular Technology Conference
LocationVirtual event
Internet address
SeriesIeee Vehicular Technology Conference

Bibliographical note

Publisher Copyright:
© 2021 IEEE.


  • Big Data analytics
  • Distributed Denial of Service (DDoS)
  • Intrusion Detection System (IDS)
  • Machine Learning
  • Mininet-Wifi


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