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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, IA<jats:sup>2<\/jats:sup>P, without any security codes or authentication. Finally, we simulate two kinds of malicious data injection attack scenarios and evaluate IA<jats:sup>2<\/jats:sup>P 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. <\/jats:p>","DOI":"10.1155\/2016\/5159739","type":"journal-article","created":{"date-parts":[[2016,5,17]],"date-time":"2016-05-17T21:02:27Z","timestamp":1463518947000},"page":"5159739","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["IA<sup>2<\/sup>P: Intrusion-Tolerant Malicious Data Injection Attack Analysis and Processing in Traffic Flow Data Collection Based on VANETs"],"prefix":"10.1177","volume":"12","author":[{"given":"Nan","family":"Ding","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China"}]},{"given":"Guozhen","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, 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