عنوان المقالة: Hierarchical Growing Neural Gas Network (HGNG)-Based Semicooperative Feature Classifier for IDS in Vehicular Ad Hoc Network (VANET)
غيث ضرغام خليل جاسم | Ghaith Khalil | 3130
نوع النشر
مجلة علمية
المؤلفون بالعربي
المؤلفون بالإنجليزي
Ayoob Ayoob, Gang Su, Ghaith khalil(AL)
الملخص الانجليزي
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. View Full-Text
تاريخ النشر
14/09/2019
الناشر
Journal of Sensor and Actuator Networks
رقم المجلد
رقم العدد
رابط DOI
https://doi.org/10.3390/jsan7030041
رابط خارجي
https://www.mdpi.com/2224-2708/7/3/41
الكلمات المفتاحية
intrusion detection system; vehicular ad hoc network; hierarchical growing neural gas network; traffic flow
رجوع