An Attacks Detection Mechanism for Intelligent Transport System

• Autor(a) principal: Pastori Valentini, Edivaldo [UNESP]
• Data de Publicação: 2020
• Outros Autores: Ipolito Meneguette, Rodolfo, Alsuhaim, Adil
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/BigData50022.2020.9378309; http://hdl.handle.net/11449/206167
• topic: denial of service; information security; Intelligent Transport Systems; intrusion detection system; VANET

The increase in computational technologies for means of transport, especially vehicles, has provided great benefits through Intelligent Transport Systems (ITS). Drivers, passengers, and pedestrians rely on computer applications that aim to protect human life, including agility in handling emergencies, improvements in traffic, and even leisure and entertainment resources. Communication and data exchange are from vehicles to vehicles (V2V) and from vehicles to road infrastructures (V2I) being carried out through the architecture of the vehicular ad hoc network (VANET). However, this type of network differs from traditional ones, as it operates in a highly dynamic environment, originated by the rapid mobility between its nodes and with short connection intervals. Wireless vehicle communication adopts the IEEE 802.11p standard, allowing vehicles to operate outside a basic set of services. Given these characteristics, numerous threats, vulnerabilities, and denial of service attacks can occur. Prioritizing the safety of life and protecting VANET against this type of attack, a security mechanism is proposed. The mechanism works to detect anomalies through a simple and robust statistical model in the search for extreme values (outliers). Median Absolute Deviation detects large amounts of MAC frames and ARP requests, characteristics of DoS / DDoS from malicious vehicles. Through extensive stages of simulations using the NS-3 and SUMO simulators, the mechanism showed excellent efficiency in detection rates and minimum rates of false positives and false negatives.