Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

TL;DR

This survey reviews misbehavior detection methods in cooperative intelligent transportation systems, focusing on insider attack detection through behavior and information analysis, highlighting current challenges and future research directions.

Contribution

It provides a comprehensive classification and overview of misbehavior detection mechanisms specifically designed for cITS, emphasizing insider attack detection beyond traditional PKI security.

Findings

Misbehavior detection mechanisms analyze attacker behavior and information semantics.

PKI-based security cannot detect insider attacks with valid keys.

The survey identifies open issues and future research trends in cITS security.

Abstract

Cooperative Intelligent Transportation Systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like PKI-based asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.