Hierarchical Growing Neural Gas Network (HGNG)-based semi-cooperative feature classifier for IDS in VANET: History Edit
Subjects: Others

In this research, new modeling strategy based Hierarchical Growing Neural Gas Network (HGNG)-semi-cooperative for feature classifier of Intrusion Detection System (IDS) in vehicular ad hoc network (VANET). The novel IDS mainly presenting a new design feature for extraction mechanism and a HGNG-based classifier. Firstly, the traffic flow features and vehicle location features were extracted in the VANET model. While in order to effectively extract location features, a semi-cooperative feature extraction is used for collecting the current location information for the neighboring vehicles through a cooperative manner and the location features of the historical location information. Secondly, HGNG-based classifier was designed for evaluating the IDS by using hierarchy learning process without the limitation of the fix lattice topology. Finally, an additional two-step confirmation mechanism is used to determine accurately the abnormal vehicle messages. In the experiment, the proposed IDS system was evaluated, observed and compared with the existing IDS. The proposed system performed a remarkable detection accuracy, stability, processing efficiency and message load.

  • Intrusion Detection System
  • vehicular ad hoc network
  • Hierarchical Growing Neural Gas Network
  • traffic flow.

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