Efficient and lightweight attack detection and prevention techniques for secure reliable and anonymous communi cation in vehicular ad hoc network

Abstract

Vehicular Ad hoc NETwork (VANET) is an ad hoc network that emerged to provide newlinetraffic management and road safety. Due to high mobility of vehicles, establishing newlinereliable end-to-end connections in VANET is hampered. Thus, VANET faces unique newlinenetwork concerns and security challenges for achieving ubiquitous connectivity and newlinesecure communication. newlineThe current research in VANET is primarily concerned with security. Without newlinesecurity, VANET is vulnerable to various attacks, potentially resulting in accidents. newlineAlso, secure communication is a prerequisite for adopting VANET communication newlineas a solution for multiple applications. To ensure secure VANET communication, newlinewe must first determine the type of attack and study its ability of disrupting network newlinecommunication. Based on the security services, the attacks are divided as follows: (i) newlineattack on authenticity, (ii) attack on availability, (iii) attack on confidentiality, (iv) newlineattack on integrity, and (v) attack on non-repudiation. The impact of these attacks newlineincludes traffic jams, accidents, and theft. newlineThis research work focuses on developing solutions for attacks such as the Sybil newlineattack that affects authenticity, black hole and gray hole attack that affects availability, newlineeavesdropping that affects confidentiality, replay attack that affects integrity, and newlinerepudiation attack that affects non-repudiation. newlineThe first contribution in this work aims to detect and evict the Sybil attacker newlinein VANET. In a Sybil attack, a malicious vehicle claims multiple identities either newlineby forging the identities of legitimate nodes or by fabricating new identities. The newlinemajority of the existing methods analyze the trust score, received signal strength, and newlineposition as parameters for Sybil node detection. Our work proposes a collaborative