Abstract:
Vehicular ad-hoc Networks (VANETs) and smart cities is an emerging area of research.
Therefore, applications based on VANET are increasing day by day. There are a num ber of applications that can utilize the VANETs architecture to benefit the end-users.
Vehicular Ad-Hoc Networks hold great promise in enhancing road safety by facilitat ing the exchange of sensor-derived information among vehicles. However, the successful
deployment of VANETs necessitates addressing critical challenges, particularly those
concerning security and privacy. The presence of malicious nodes within the network
poses significant threats to its security and privacy. The focus of this thesis is to in vestigate the security and privacy concerns encountered by vehicles in VANETs, with
specific emphasis on countering Sybil Attacks. These attacks involve a malicious vehicle
illicitly acquiring multiple identities, intensifying the security vulnerabilities present in
VANETs. Efforts are directed towards comprehending and mitigating these challenges
to ensure the safe and reliable operation of VANETs.
The Sybil attacker sends numerous messages with apparent false identities (malicious
nodes) to other vehicles in the network. This creates an optical illusion or confusion
among the other vehicles on the same track. Sybil Nodes can cause serious damage
by sharing/injecting erroneous data into the network. The proposed scheme makes the
network secure and protects it from the harmful effects of Sybil’s attack. The proposed
scheme makes VANETs more secure by making them fault-tolerant and resisting the
presence of detectable and re-portable Sybil nodes. Because VANETs are dynamic and
fast-moving, a data-driven scheme is proposed that can determine whether a node is
Sybil or normal by involving neighboring nodes.
