Multiple Attack Layered Detection Mechanism in Vehicular Named Data Networking
Na Fan, Yuxin Gao, Jialong Li, Liping Ye · 2024
Vehicular Named Data Networking is subject to a variety of attacks, including Interest Flooding Attack, On-Off Attack, and Collusive Interest Flooding Attack, which can affect the communication efficiency of the network. For scenarios where the three aforementioned attacks coexist, a multiple attack layered detection mechanism is proposed. The mechanism is divided into two layers. The first layer determines whether there are attacks by self-detecting of vehicle nodes and roadside units. The second layer identifies the attack type according to the location distribution of the attacked vehicles and the network traffic. Simulation experiments show that the mechanism proposed in this paper improves the attack identification accuracy by an average of 20% and up to 27%, and reduces the false alarm rate by an average of 46% and up to 56% compared to existing methods.