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- W2181317697 abstract "With data proliferation at an unprecedented rate, data accessibility while in motion has increased the demand of VANETs of late. VANETs use moving cars as nodes to create a mobile network. VANETs are designed with the goals of enhancing driver safety and providing passenger comfort. Providing security to VANETs is important in terms of providing user anonymity, authentication, integrity and privacy of data. Attacks in VANETs include the Sybil attack, DDoS attack, misbehaving and faulty nodes, sinkhole attack, spoofing, traffic analysis attack, position attack and illusion attack. Illusion attack is a recent threat in which the attacker generates fraud traffic messages to mislead the passengers thereby changing the passenger's driving behaviour. Thus, the attacker achieves his goal by moving the target vehicle along a traffic free route and creating traffic jams in areas where he wishes. Illusion attack is devised mainly by thieves and terrorists who require a clean get away path. The existing method used to prevent Illusion attack is the Plausibility Validation Network (PVN). In PVN the message validation is based on a set of rules depending on the message type. This is an overhead as the rule set for all possible message types have to be stored and updated in the database. Hence, an efficient mechanism is required to prevent illusion attacks. In this paper, our proposed system: TRUC verifies the message by a Message Content Validation (MCV) Algorithm thus ensuring the safety of the car drivers and passengers. The possibilities of attacker creating an Illusion attack is explored in all dimensions and the security goals are analyzed by our proposed design, TRUC" @default.
- W2181317697 created "2016-06-24" @default.
- W2181317697 creator A5090385081 @default.
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- W2181317697 date "2014-01-01" @default.
- W2181317697 modified "2023-09-25" @default.
- W2181317697 title "Truc: Towards Trusted Communication For Emergency Scenarios In Vehicular Adhoc Networks (vanets) Against Illusion Attack" @default.
- W2181317697 doi "https://doi.org/10.5339/qfarc.2014.itpp1109" @default.
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