Vehicular Ad-hoc Networks (VANETs) are gaining much interest and research efforts over recent years for it offers enhanced safety and improved travel comfort. However, security threats that are either seen in the ad-hoc networks or unique to VANET present considerable challenges. In this paper, we are presenting the intrusion detection classifier for VANET base on pre-processing feature extraction. This ID infrastructure novel is mainly introducing a new design feature for extraction mechanism a pre-processing feature-based classifier. In the beginning, we will extract the traffic stream structures and vehicle location features in the VANET model. Later an Algorithm Pre-processing feature-based classifier was designed for evaluating the IDS by using hierarchy learning process. Finally, an additional two-step validation mechanism was used to determine the abnormal vehicle messages accurately. The proposed method has better finding accuracy, stability, processing efficiency, and communication load.
History
Volume
1113
Pagination
3-22
Location
Melbourne, Victoria
Start date
2019-11-27
End date
2019-11-29
eISSN
1865-0937
ISBN-13
9783030343521
Language
eng
Publication classification
E1 Full written paper - refereed
Editor/Contributor(s)
Ram Mohan Doss R, Piramuthu S, Zhou W
Title of proceedings
FNSS 2019 : Future Network Systems and Security 5th International Conference, FNSS 2019 Melbourne, VIC, Australia, November 27–29, 2019 Proceedings
Event
Future Network Systems and Security. Conference (2019 : Melbourne, Victoria)
Publisher
Springer
Place of publication
Cham, Switzerland
Series
Communications in Computer and Information Science