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

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journal contribution
posted on 2018-09-01, 00:00 authored by A A Ayoob, G Su, Ghaith Dh Khalil
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.

History

Journal

Journal of sensor and actuator networks

Volume

7

Issue

3

Article number

41

Pagination

1 - 19

Publisher

MDPI

Location

Basel, Switzerland

eISSN

2224-2708

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, the authors