SCIRP Mobile Website
Paper Submission

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Johansson, K.H., Aurngren, M. and Nielsen, L. (2005) Vehicle Applications of Controller Area Network. In: Hristu-Varsakelis, D. and Levine, W.S., Eds., Handbook of Networked and Embedded Control Systems, Springer, Berlin, 741-765.

has been cited by the following article:

  • TITLE: Classification Approach for Intrusion Detection in Vehicle Systems

    AUTHORS: Abdulaziz Alshammari, Mohamed A. Zohdy, Debatosh Debnath, George Corser

    KEYWORDS: CAN-Bus, IDS, KNN, SVM, Machine Learning, DoS Attack, Fuzzy Attack

    JOURNAL NAME: Wireless Engineering and Technology, Vol.9 No.4, October 31, 2018

    ABSTRACT: Vehicular ad hoc networks (VANETs) enable wireless communication among Vehicles and Infrastructures. Connected vehicles are promising in Intelligent Transportation Systems (ITSs) and smart cities. The main ob-jective of VANET is to improve the safety, comfort, driving efficiency and waiting time on the road. VANET is unlike other ad hoc networks due to its unique characteristics and high mobility. However, it is vulnerable to various security attacks due to the lack of centralized infrastructure. This is a serious threat to the safety of road traffic. The Controller Area Network (CAN) is a bus communication protocol which defines a standard for reliable and efficient transmission between in-vehicle parts simultaneously. The message moves through CAN bus from one node to another node, but it does not have information about the source and destination address for authentication. Thus, the attacker can easily inject any message to lead to system faults. In this paper, we present machine learning techniques to cluster and classify the intrusions in VANET by KNN and SVM algorithms. The intrusion detection technique relies on the analysis of the offset ratio and time interval between the messages request and the response in the CAN.