Sybil Attack Detection in VANET Using Machine Learning Approach

Abstract

VANET (Vehicular Ad-hoc Network) is a subclass of MANET in which many cars can connect with one another via node to node or equipment erected on the side of the road. However, due to the adaptability of centres and the unexpected trade in geography, there may be opportunities for attacks in VANET. One of the ostensible assaults is the Sybil attack, in which the attacker fabricates unequivocally unique equal personalities to undermine the value of VANET. Sybil creates fictitious identities inside the community as well in order to sabotage attempts to mediate conversations between community nodes. Sybil assaults have an impact on carrier transportation in relation to things like traffic congestion, road safety, and multimedia entertainment. VANETs therefore announce a security mechanism to protect you from Sybil attacks. In this regard, this work puts forth the SDTC method, which completely relies on machine learning techniques to prevent Sybil assaults in VANETs. In order to reduce identification time, increase detection accuracy, and enhance scalability, the SDTC (Sybil node detecting the use of Classification) mechanism uses a few vehicle-specific Extreme Learning Machine (ELM) features. The results suggest that SDTC is a suitable strategy to reduce Sybil assaults and sustain provider service in VANETs.