# A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber-Physical Attacks
## 3 Vulnerability Assessment Approach  
### Overview of Vulnerability Assessment Approach  
The proposed vulnerability assessment approach in this paper assesses the vulnerability of an IoT-enabled transportation network which is subjected to cyber-physical attacks on the sensing layer. The sensing layer includes both the physical infrastructure and the embedded IoT-based WSN. The novel approach relies on a topological vulnerability assessment method in the form of a graph [59]. Specifically, the graph is an ordered pair G(N, E), including a set N of nodes and a set E of edges. Depending on the evaluated transportation network, these can be undirected (i.e., no order is assumed between the nodes linked by an edge) or directed (i.e., a start and end node of each edge is assumed) and unweighted (i.e., all edges have the same weight) or weighted (i.e., the edges may have different weights). In this approach, the set of N nodes (i.e., n\({}_{i}\)) represents the IoT-enabled transportation infrastructure of the transportation network that is subjected to the cyber-physical attack, and the set of E edges (i.e., e\({}_{i}\)) represents the distance between them.  
As shown in Figure 2, the approach includes five activities. The first activity (i.e., _selection of a sensing area cyber-physical attack scenario_) includes the selection of a cyber-physical attack scenario that takes place at the sensing area. The activity is facilitated by using either individual or combined publicly available catalogues of common attack patterns (e.g., CAPEC [60]) and current theoretical [61] or experimental studies [62], all based on IoT devices. The second activity (i.e., _Division of cyber-physical attack scenario into vulnerability states in physical and cyber space_) is based on the BN attack graph that details the vulnerability states, in both the physical and cyber space, that the attacker needs to exploit in order to succeed in the selected cyber-physical attack scenario (i.e., first activity). This approach goes beyond previous works, as it integrates the cyber and physical space for the first time. For example, in a network attack, the attacker should gain physical proximity to the IoT-based WSN by overcoming the physical security of the IoT-enabled transportation infrastructure. The third activity in Figure 2 (i.e., _development of conditional probability table for every node i_) necessitates the development of a conditional probability table (CPT), as part of the BN [63], that will enable the calculation of the probability of the successful exploitation of vulnerability states for the selected attack scenario (i.e., refer to the first and second activity) for every node, \(i\). The first, second and third activities are described in more detail in Section 3.2. The fourth activity (i.e., _calculation of probability indicator (PI)_) includes the calculation of the proposed PI that will enable assessors to assign a probability score to every vulnerability state, \(i\). The ratio considers both a detailed attacker profile and the physical and cyber control barriers that protect every vulnerability state. The fifth activity (i.e., _removal of nodes based on probability-based rank and vulnerability assessment as drop in efficiency_) describes the measurement of vulnerability of the transportation network as a loss of efficiency [64] after the removal of the highest probability-based ranked nodes. The fifth activity is further described in Section 3.3.