Identification of vulnerable IoT enabled transportation infrastructure into a cyber-physical transportation network

Vulnerability of transportation networks to cyber-physical attacks is of major concern, due to security issues of Internet of Things devices in the sensing layer of transportation infrastructure. However, traditional vulnerability approaches in the civil engineering domain, overlook the integration of physical and cyber space. In this paper, we propose a new approach to identify vulnerable Internet of Things enabled transportation infrastructure and assess the vulnerability of transportation networks. The approach relies on a Bayesian network attack graph that enables the probabilistic modeling of vulnerability states in physical and cyber space. Based on a probability indicator that considers the attacker characteristics and the control barriers we identify the vulnerable transportation infrastructure and assess the vulnerability, as a drop in transportation network efficiency. Monte Carlo simulations are performed as a method to evaluate the results of a case study transportation network. The results are of interest to stakeholders in the transportation domain and indicate the increasing susceptibility due to deficient control barriers in both spaces.