| Title | An Intelligent Mechanism for Sybil Attacks Detection in VANETs |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Quevedo, C. H. O. O., Quevedo, A. M. B. C., Campos, G. A., Gomes, R. L., Celestino, J., Serhrouchni, A. |
| Conference Name | ICC 2020 - 2020 IEEE International Conference on Communications (ICC) |
| Date Published | jun |
| Keywords | Acceleration, Complexity theory, composability, data integrity, data privacy, distributed network, extreme learning machine, Extreme Machine Learning, feedforward neural nets, fixed road-side components, machine learning, Metrics, privacy, pubcrawl, Resiliency, Scalability, security, Sybil attack detection, sybil attacks, Sybil Attacks., SyDVELM mechanism, telecommunication computing, telecommunication network topology, telecommunication security, user privacy information, VANET, VANETs, vehicular ad hoc networks |
| Abstract | Vehicular Ad Hoc Networks (VANETs) have a strategic goal to achieve service delivery in roads and smart cities, considering the integration and communication between vehicles, sensors and fixed road-side components (routers, gateways and services). VANETs have singular characteristics such as fast mobile nodes, self-organization, distributed network and frequently changing topology. Despite the recent evolution of VANETs, security, data integrity and users privacy information are major concerns, since attacks prevention is still open issue. One of the most dangerous attacks in VANETs is the Sybil, which forges false identities in the network to disrupt compromise the communication between the network nodes. Sybil attacks affect the service delivery related to road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, called SyDVELM, to detect Sybil attacks in VANETs based on artificial intelligence techniques. The SyDVELM mechanism uses Extreme Learning Machine (ELM) with occasional features of vehicular nodes, minimizing the identification time, maximizing the detection accuracy and improving the scalability. The results suggest that the suitability of SyDVELM mechanism to mitigate Sybil attacks and to maintain the service delivery in VANETs. |
| DOI | 10.1109/ICC40277.2020.9149371 |
| Citation Key | quevedo_intelligent_2020 |
An Intelligent Mechanism for Sybil Attacks Detection in VANETs