Hierarchical growing neural gas network (HGNG)-based semicooperative feature classifier for IDS in vehicular ad hoc network (VANET)

Ayoob, Ayoob Azeez, Su, Gang and Khalil, Gaith 2018, Hierarchical growing neural gas network (HGNG)-based semicooperative feature classifier for IDS in vehicular ad hoc network (VANET), Journal of sensor and actuator networks, vol. 7, no. 3, pp. 1-19, doi: 10.3390/jsan7030041.

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Title Hierarchical growing neural gas network (HGNG)-based semicooperative feature classifier for IDS in vehicular ad hoc network (VANET)
Author(s) Ayoob, Ayoob Azeez
Su, Gang
Khalil, GaithORCID iD for Khalil, Gaith orcid.org/0000-0002-9951-8285
Journal name Journal of sensor and actuator networks
Volume number 7
Issue number 3
Article ID 41
Start page 1
End page 19
Total pages 19
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2018-09
ISSN 2224-2708
Keyword(s) intrusion detection system
vehicular ad hoc network
hierarchical growing neural gas network
traffic flow
Summary In this research, new modeling strategy based hierarchical growing neural gas network (HGNG)-semicooperative for feature classifier of intrusion detection system (IDS) in a vehicular ad hoc network (VANET). The novel IDS mainly presents a new design feature for an extraction mechanism and a HGNG-based classifier. Firstly, the traffic flow features and vehicle location features were extracted in the VANET model. In order to effectively extract location features, a semicooperative 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, the HGNG-based classifier was designed for evaluating the IDS by using a hierarchy learning process without the limitation of the fix lattice topology. Finally, an additional two-step confirmation mechanism is used to accurately determine 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.
Language eng
DOI 10.3390/jsan7030041
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, the authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121270

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