IA2P: Intrusion-Tolerant Malicious Data Injection Attack Analysis and Processing in Traffic Flow Data Collection Based on VANETs

Abstract

Several studies investigating data validity and security against malicious data injection attacks in vehicular ad hoc networks (VANETs) have focused on trust establishment based on cryptology. However, the current researching suffers from two problems: (P1) it is difficult to distinguish an authorized attacker from other participators; (P2) the large scale of the system and high mobility set up an obstacle in key distribution with a security-based approach. In this paper, we develop a data-centric trust mechanism based on traffic flow theory expanding the notion of trust from intrusion-rejecting to intrusion-tolerant. First, we use catastrophe theory to describe traffic flow according to noncontinuous, catastrophic characteristics. Next, we propose an intrusion-tolerant security algorithm to protect traffic flow data collection in VANETs from malicious data injection attacks, that is, IA2P, without any security codes or authentication. Finally, we simulate two kinds of malicious data injection attack scenarios and evaluate IA2P based on real traffic flow data from Zhongshan Road in Dalian, China, over 24 hours. Evaluation results show that our method can achieve a 94% recognition rate in the majority of cases.