Complex attack detection scheme using history trajectory in internet of vehicles

The internet of vehicles technology provides convenience to drivers and prevents traffic accidents via wireless communication between road infrastructure and autonomous vehicles by sharing real-time traffic information. However, attackers can easily penetrate networks by exploiting the vulnerabilities of wireless communications. An attacker can falsify real-time traffic information and transmit it to a vehicle, causing traffic jams or preventing autonomous vehicles from receiving legitimate real-time traffic information. If autonomous vehicles do not receive accurate information, the arrival time at the destination can be affected, and accidents due to incorrect driving can occur. Because traffic accidents can cause casualties, they must be prevented. Various schemes have been proposed to detect attacks that occur on the internet of vehicles, and these security schemes can prevent traffic accidents by detecting attacks at high speeds. However, the existing schemes focus on quickly identifying a single attack but encounter difficulties when attempting to detect complex attacks that occur simultaneously. The proposed scheme uses a history trajectory to detect complex attacks. The proposed scheme stores behavioral information on all vehicles and road infrastructure using a control center. This information becomes a history trajectory that is used to detect attacks. Thereafter, when the vehicle is abnormally driven, the control center analyzes its driving path. When analyzing the vehicle driving process, the control center determines that an attack is being attempted when the road infrastructure or a vehicle makes an erroneous state transition. In addition, the type of attack is analyzed to identify compromised vehicles or road infrastructure and take measures to prevent further problems. Thus, the proposed scheme can detect complex attacks through history trajectory analysis. The experimental results demonstrate that in 80% of attempted attacks, the proposed scheme detects complex attacks with a probability of 97.56%.