## 1 Introduction  
In recent times, the growth of the internet of things (IoT) and information communication technologies (ICT) have revolutionized the modern era and critical infrastructures (CIs), including smart manufacturing, healthcare, energy sector, education, and maritime transportation, among others [1, 2]. On the one hand, modern communication and electronic technologies have provided many facilities to individuals and nations in different CIs. On the other hand, safeguarding security and safety are essential requirements to offer authenticated operations against possible cyber threats and crises within the respective CIs [3]. Generally, the security mechanisms focus on recognizing and managing risks interrelated with accessibility, privacy, and integrity of devices in CIs. However, safety approaches are inclined to predict, classify, and resolve the vulnerabilities linked with the safety of humans, systems, and infrastructures. Therefore, integrating both aspects can help identify potential vulnerabilities and threats and the evaluate probable risks associated with the security and safety of CIs. The incorporation of security and safety aspects has received massive attention worldwide [4, 5]. Recent research shows that safety, especially cybersecurity, share interdependencies in many products, especially cyber-physical systems (CPS) [6]. Besides safety regulations interfering in possible security solutions, a fundamental problem is the rising number of cybersecurity threats that negatively impact the affected functional safety and reliability of systems [7]. In safety-sensitive environments, such as the in railway, aircraft, or automotive industries, the consideration of security is widespread [8]. Decision-makers must determine whether the identified issue is due to an attack or technical failure. A precise diagnosis is crucial for an effective response to identified problems. For example, fixing or exchanging the module responsible for the observed issue could be a reasonable response tactic for a technical failure. Simultaneously, blocking an attack vector, utilizing an identified adversary-caused problem, might be an efficient response monitoring strategy. If the decision-makers can calculate that the apparent problem is an attack, the efficient response policies to resist each attack vector would be dissimilar. For example, the operative response approach for an information manipulation threat on the device could acquire data integrity checks. In contrast, the active response approach against the physical tampering of the device would augment access control. Remarkably, the decision supporting the regulation of the utmost probable root cause for evident problems is not available. In these conditions, Bayesian networks (BNs) could be helpful to solve this problem, mainly cybersecurity and safety applications [4, 5, 6, 7]. In BNs, both qualitative and quantitative components are included, such as the directed acyclic graph (DAG) and conditional probability table (CPT), for each node in the DAG, respectively [8]. Furthermore, the graph theory (GT) and neutral network are also incorporated with the safety aspects of network security [9]. Some systematic literature reviews (SLRs) or literature reviews related to safety and security, based on BNs or GT, are available in the literature. [PERSON] et al. presented a systematic review of safety and security measures for machine learning-enabled agricultural applications. The focus of this study was BN approaches; however, GT was not addressed [10]. [PERSON] et al. performed a systematic review on blockchain-oriented security outbreak resilience systems for self-governing automobiles. The main limitations are that vehicle applications and their safety aspects are not considered [11]. [PERSON] et al. conducted SLR on 17 BN-based models for integrating cybersecurity and safety measures in different applications [12]. The main drawback of this SLR includes that it merely emphasized BN models; however, GT was not addressed. [PERSON] et al.