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

Nan Ding,Wei Zhang,Guozhen Tan

doi:10.1155/2016/5159739

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.

Highlights

vehicular ad hoc networks (VANETs) are emerging as an effective new tool to monitor the physical world [1]
(1) develop an intrusion-tolerant security mechanism against injection attacks without security codes or authentication, IA2P, and this extends the notion of security from intrusion-rejecting to intrusiontolerant, and, this approach is more useful in practice than traditional trust establishment based on cryptology; (2) expand cusp catastrophe theory to analyze traffic flow data profiling and this is more suitable for traffic flow data characteristics in most traffic scenarios, allowing for effective analysis of injection attacker’s activities; (3) integrate batch estimation filters with coefficient selfadjustment to meet traffic flow time-varying volatility in order to generalize injection attack analysis and processing
We firstly identify a previously unknown vulnerability in the current techniques aimed at security establishment against the malicious data injection attack in VANETs

Summary

Introduction

VANETs are emerging as an effective new tool to monitor the physical world [1]. They gather traffic flow data (GPS, speed measurements, etc.) from sensor platforms in vehicles and relay these data via vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communication. To use a cipher for TA or MC, every participator (vehicular or fixed roadside infrastructure) requires some kind of a shared secret, providing various methods of secret key distribution [5, 6] These researches are suffering from the following problems: (1) it is difficult to distinguish an authorized attacker from other participators and (2) the large scale of the system and high mobility set up an obstacle in key distribution with a security-based approach. We used traffic flow characteristics to develop an intrusion-tolerant security mechanism to protect traffic flow data collection in VANETs against injection attacks. Our study is innovative because we (1) develop an intrusion-tolerant security mechanism against injection attacks without security codes or authentication, IA2P, and this extends the notion of security from intrusion-rejecting to intrusiontolerant, and, this approach is more useful in practice than traditional trust establishment based on cryptology;.