## 2 Related Work  
There are a number of studies that focus on attack path discovery. Existing research treats attack path discovery as an important stage focused on identifying and understanding the routes in a network that potential attackers might follow to gain unauthorized access to a system. This section provides an overview of the existing related work. The attackers first infiltrate vulnerable hosts to access the system and use the previous attack result as a precondition and repeat this process until they achieve the level of control desired. Previous studies [4; 5] aimed to evaluate all possible attack paths in a network and to predict future attacks by combining components from a collaborative filtering recommender systems and attack path discovery approaches using Naive Bayes and random forest. This method searches for all non-circular attack paths that exist between assets that belong to the network and induces a model where an attacker can gain access to information system sources following a directed path. The security weaknesses of an asset follow the vulnerability assessment, conducting a thorough analysis of the existing and potential threat landscape within a network that can be valued. A stochastic analysis is considered for the evaluation of cyberattack paths through sophisticated methods to measure the probability and acceptability of faults [6]. The development of threat scenarios can delineate the underlined threat landscape and thus facilitate the threat knowledge and improve the visualization [7]. Other groups consider Attack Trees or Attack Graphs, which are widely used approaches for considering threat analysis during the risk assessment process. The attack graph network measurement can be classified into structure and probability-based metrics to quantify network security [8] to illustrate the network's agility in taking preemptive measures to respond to attacks and stochastic-based metrics to estimate large nodes of networks. Another work focuses on attack modelling as a useful tool in risk assessment for cyber physical systems based on the attack vector within the technical and operational environment [9]. Attack graphs are considered a series of exploitation of atomic attacks, which can drive the process to an undesirable state and are used for various applications including threat detection and forensic analysis [10]. More recently, research has appeared that focuses on discovering and analysing attack paths using threat intelligence and vulnerability exploitation. The exploration of attacks based on threat intelligence data is collected using cloud-based web service in [11]. The attack surface classification methodology of mobile malware with known threat actorsthrough automated tactics, techniques and procedures (TTP) and IoC analysis is described in [12]. Hence, IoC is considered as a key parameter to analyse the attack surface and attackers' motivations. A further study [13] focuses on just certain parts of the network to identify and generate attack graphs. For instance, in this strategy, they assume that there is a privilege over an asset across the network. If it is accurate, that means that the user gained access to the asset. An attack path discovery in the dynamic supply chain is proposed using the MITIGATE method in [14]. The approach considers a dynamic risk management system to detect the vulnerabilities that can deliver attack paths based on certain criteria. It considers attacker capability, attack path and its length, and knowledge base for analysing the attack paths. Another work proposes a recommended system that focuses on possible methods that can be used to classify future cyberattacks in terms of risk management [15]. This approach considers the exploitability features for attack path generation and uses a multi-level collaborative filtering method to predict the future attacks.