NDLI: A new Intrusion Detection Framework for Vehicular Networks

Content Provider	IEEE Xplore Digital Library
Author	Sedjelmaci, H. Senouci, S.M.
Copyright Year	2014
Description	Author affiliation: DRIVE Lab., Univ. of Bourgogne, Nevers, France (Sedjelmaci, H.; Senouci, S.M.)
Abstract	In this paper, we design and implement a new Intrusion Detection Framework for Vehicular Networks (IDFV). These networks are vulnerable to various security attacks due to the lack of centralized infrastructure. The aim of our framework is then to secure them against the most dangerous routing attacks that have a high severity damage such as selective forwarding, black hole, wormhole, packets duplication and resource exhaustion attacks that can target such networks. IDFV relies on a set of detection and eviction techniques to detect, in a short delay, malicious vehicles with a high accuracy and eject them. Furthermore, IDFV applies a robust reputation schema to evaluate vehicles' trust level. We analyze the performances of our framework using NS-3. Simulation results show that IDFV exhibits a high level of security i.e. high detection rate, low false positive rate and a fast attacks' detection compared to detection frameworks proposed in current literature.
Starting Page	538
Ending Page	543
File Size	440619
Page Count	6
File Format	PDF
ISBN	9781479920037
DOI	10.1109/ICC.2014.6883374
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Intrusion detection Monitoring Ad hoc networks Routing Accuracy Attacks Vehicular networks Clustering Data collection
Content Type	Text
Resource Type	Article

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