Application of Secondary Information for Misbehavior Detection in VANETs

Abstract

Safety applications designed for Vehicular Ad Hoc Networks (VANETs) can be compromised by participating vehicles transmitting false or inaccurate information. Design of mechanisms that detect such misbehaving nodes is an important problem in VANETs. In this paper, we investigate the use of correlated information, called "secondary alerts", generated in response to another alert, called as the "primary alert" to verify the truth or falsity of the primary alert received by a vehicle. We first propose a framework to model how such correlated secondary information observed from more than one source can be integrated to generate a "degree of belief" for the primary alert. We then show an instantiation of the model proposed for the specific case of Post-Crash Notification as the primary alert and Slow/Stopped Vehicle Advisory as the secondary alerts. Finally, we present the design and evaluation of a misbehavior detection scheme (MDS) for PCN application using such correlated information to illustrate that such information can be used efficiently for MDS design.

A survey of local/cooperative-based malicious information detection techniques in VANETs

“…The main drawback of these schemes are when malicious nodes increase than honest nodes which creates a false result [3]. In VANETs, low density of vehicles also decreases the performance of cooperative detection schemes [9, 65,68] as shown in Table 2.…”

“…The degree of belief which is 0 shows a false event; however, the performance of this scheme degraded due to high speed. The minimum density of nodes also effect truism of the scheme [68].…”

Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks

“…Thus, this provides scope for better observations and decision making. Decision Parameter, D P is calculated for all the nodes considered for verifier selection by taking into account the load, distance and distrust value of the node by the following equation (2).…”

A Robust Security Algorithm for VANETs

“…Vulimiri et al [5] have advised a probabilistic wrongdoing detection approach that is depend on the secondary data. These alerts square measure build correspondence to primary alerts.…”

“…Of attacks like part, timing, illusion, DOS [10], Sybil that not solely influences the vehicles and deriver's privacy however additionally affects the traffic safety [1][13]. In some cases, it's going to ends up in loss of life to confirm the traffic safety the VANET wants some appropriate security techniques that may assure protection across distinct misbehaviours and malicious nodes that influence the protection of the VANET [5] [7].…”