# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 3 Methodology  
This section provides an in-depth explanation of the methodology used to implement the DICOM simulator in Python. It outlines the key steps involved in establishing communication with the PACS server, configuring the necessary libraries, and sending DICOM images. To understand the interactions between DICOM modalities and PACS servers, a mathematical model can be formulated to describe the association between DICOM modalities and PACS servers. The association between a DICOM modality and a PACS server involves several parameters that define the communication and data exchange. Let us consider a DICOM modality \(M\) and a \(PACS\) server. The relationship between \(M\) and \(PACS\) can be represented using Equation (1):  
\[A(M,S)=\{AE_{M},AE_{PACS},SOP_{\text{class}},TS\} \tag{1}\]  
where \(A(M,S)\) represents the association between the modality \(M\) and PACS server \(PACS\), \(AE_{M}\) is the Application Entity representing the DICOM modality in the network, \(AE_{S}\) is the Application Entity representing the PACS server in the network, \(SOP_{\text{class}}\) denotes the SOP Class specifying the type of medical image or information being exchanged, and \(TS\) is the transfer syntax that defines how the data are encoded and decoded during transmission. Application Entities (AE) can be represented as nodes in a network graph. Each node corresponds to an AE, which can be either the DICOM modality or the PACS server. The communication between these nodes is facilitated by the DICOM protocol, as presented in Equation (2). Equation (2) describes nodes and edges as the elements of a graph, where \(AE_{M}\) and \(AE_{S}\) are nodes representing entities within the network and edges \((AE_{M},AE_{S})\) signify a connection or relationship between these nodes:  
Nodes: \[N=\{AE_{M},AE_{S}\}\text{ and Edges: }E=\{(AE_{M},AE_{S})\}\] (2)where \(N\) is the set of nodes representing the DICOM modality and PACS server and \(E\) is the set of edges indicating the communication links between the modality and the server. SOP classes define the type of medical image or object being exchanged, while the Transfer Syntax specifies how the data are encoded and compressed for transmission. The association of SOP classes and transfer syntax is presented in Equation (3):  
\[A(SOP_{\text{class}},TS)=\{SOP_{\text{class}},TS\} \tag{3}\]  
where \(A\) is a function that takes two parameters, namely, \(SOP_{\text{class}}\) and \(TS\), while \(SOP_{\text{class}}\) represents a parameter related to the class of SOP in the context of DICOM, \(TS\) similarly represents the Transfer Syntax in DICOM, \(SOP_{\text{class}}\) represents the specific type of medical image or information, \(TS\) denotes the transfer syntax used for encoding and decoding, and the function \(A\) in Equation (3) takes two DICOM-related parameters and constructs a set containing their values. DICOM communication often involves sensitive patient data, necessitating secure protocols [44]. Transport Layer Security (TLS) and Secure Sockets Layer (SSL) ensure encrypted data transmission and authentication, while Internet Protocol Security (IPSec) offers secure network-layer communication. DICOM Secure Communication Profiles define secure communication guidelines for enhanced data privacy and integrity in medical imaging networks. The choice of protocol depends on security needs, regulatory compliance, and infrastructure compatibility. The dependency graph (Figure 1) depicts the DICOM architecture followed in this research. These concepts, although only briefly summarized in this section, are explored in greater depth later on, shedding light on their profound influence within the field of medical imaging. At the core of DICOM is the AE, which designates network-connected devices such as imaging systems, workstations (Figure 1), or healthcare modalities. Playing a crucial role in simplifying accessibility for healthcare professionals, PACS servers are responsible for the storage, retrieval, and orchestration of medical images and their associated data.