Detecting and correcting malicious data in VANETs

By Philippe Golle, Daniel H. Greene, Jessica Staddon. International Workshop on VehiculAr Inter-NETworking, 2004

607 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/55de41bdb6d93c7fb6f9ffb49f951c7608e6a7c3

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In Artificial Intelligence

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Security and Cooperation in Wireless Networks (2007, background): “Mechanisms for the detection of malicious data [154] can be leveraged to spot vehicles generating these data (since all messages are signed).”
Efficient and robust pseudonymous authentication in VANET (2007, background): “A number of recent works are concerned with different aspects of security and privacy of vehicular networks, either outlining challenges [36], [28], describing particular attacks [21], [10] or more general attack overviews [5], offering general suggestions towards solutions [17], [30], proposing mec”
AMOEBA: Robust Location Privacy Scheme for VANET (2007, background): “5km street separation, 3m lane separation 2-lane one-way street or 2-lane two-way street, 3m lane separation Traffic volume 3000 vehicles/hour/lane 1000 vehicles/hour/street Node Density [100, 500] vehicles/lane [50, 250] vehicles/street Node Speed [72, 144] km/hr [36, 72] km/hr Node Acceleration [0”
An Identity-Based Security System for User Privacy in Vehicular Ad Hoc Networks (2010, background): “Miscellaneous: Some other requirements pertinent to VANET security include data consistency, availability, position verification, efficiency, and scalability, and are discussed in [34], [35], [5], [36], [37] and [1], respectively.”
Mobeyes: smart mobs for urban monitoring with a vehicular sensor network (2006, methodology): “This can be effectively represented by using a spatial graph as shown in [8] (see Figure 17).”
Survey on VANET security challenges and possible cryptographic solutions (2014, background): “[68] treat correcting malicious data entered in VANETs.”
ECPP: Efficient Conditional Privacy Preservation Protocol for Secure Vehicular Communications (2008, background): “Extensive research efforts have been made by both industry and academia to investigate some key issues in vehicular networks [3]–[7], where security assurance and privacy preservation are two primary concerns [8]–[11].”
Eviction of Misbehaving and Faulty Nodes in Vehicular Networks (2007, methodology): “This assumption (also elaborated in [13], [3]) may appear limiting; but it is reasonable if we consider the existing transportation systems where the actual percentage of attackers is very low.”
The security of vehicular ad hoc networks (2005, methodology): “In Section 3 we describe the system model that we subsequently use to provide a threat analysis in Section 4 and the corresponding solutions in Section 5.”
Providing VANET security through active position detection (2007, methodology): “Resource-based methods test vehicles’ resources, such as radio resources [26,8], computational resources (vehicles failing to solve a puzzle are identiﬁed as fakes) [7], and identiﬁcation resources (vehicles whose MAC and IP addresses are not recorded in a proﬁle are identiﬁed as fakes) [27].”
On Data-Centric Trust Establishment in Ephemeral Ad Hoc Networks (2008, methodology): “[10] proposes a framework for data validation in VANETs;”
An RSSI-based scheme for sybil attack detection in wireless sensor networks (2006, background): “Solutions [4], [10] that adopt key exchange to vouch identification severely effect the energy consumption due to distribution and piggybacking of randomly generated keys in messages.”
TACKing Together Efficient Authentication, Revocation, and Privacy in VANETs (2009, background): “Short-term linkability is a desirable property in several VANET applications [13].”
SECURING VEHICULAR COMMUNICATIONS (2006, background): “A first example of such a mechanism is presented in [4], where the vehicle has a model to which it compares received data before classifying it as truthful, malicious, or unintentionally incorrect.”