## 2 Related Works  
Anomaly detection within the maritime domain has been researched through the use of Automatic Identification System (AIS) data, trajectory analysis, cloud-based frameworks, and ML-based techniques to enhance the level of security and situational awareness in the maritime environment. [PERSON] et al. (2022) [6] made note that the methods for detecting anomalies in the AIS tracks are course deviation and unexpected port arrivals as features for showing suspicious operations and keeping the maritime domain safe. Recently, [PERSON] et al. (2024) [3] proposed an approach for network intrusion detection using deep autoencoders in a Deep Nested Clustering Autoencoder (DNCAE) model to enhance anomaly detection through the learning of a better representation of normal network data in latent space and creating tighter regions of clusters using the K-means clustering technique. [PERSON] et al. (2022) [7] proposed a solution to estimate the Remaining Useful Life for aircraft engines adopting an autoencoder-based Deep Belief Network model. [PERSON] et al. (2021) [8] conducted research for the detection of sensor data anomalies from maritime engines. They used ensemble learning techniques to combine various machine learning models to increase the overall accuracy and robustness of the developed anomaly detection system, especially in accurately identifying network intrusion among IT systems used in the maritime industry. [PERSON] et al. (2023) [9] demonstrated that the K-means clustering method was able to effectively detect ships or those sets of vessels that turn off the AIS transceivers to perform illegal activities. Other studies focused on the trajectories' analysis instead of focusing on the AIS data to discover anomalous behaviours and intrusions. [PERSON] et al. (2023) [10] developed a Transfer Learning-based Trajectory Anomaly Detection strategy for IoT-empowered Maritime Transportation Systems to explore the spatial similarity between normal trajectories and train the trajectory anomaly detection model. [PERSON] et al. (2020) [11] proposed a novel approach to model the uncertainty of the vessels' movements while following a route by adopting clustering techniques based on the estimation of the probability of a deviation from the given trajectory. On the other side, cybersecurity threats affecting the communication channel between the ship and the shore, as well as the ones affecting the land information system, must be considered. [PERSON] et al. (2023) [5] researched to better understand both enabling factors and challenges impacting the effectiveness of Maritime Security Operation Centres (M-SOC), which are the ancestors of the Remote Operations Centres. [PERSON] et al. (2022) [12] and [PERSON] et al. (2024) [13] conducted studies to identify the cyberthreats affecting the maritime domain in its entirety and discuss risk assessment methods and possible recommendations and countermeasures. [PERSON] and [PERSON] (2014) [14] conducted a security evaluation of the AIS implementations and communication protocols specifications by identifying a set of threats related to both the software and the hardware equipment such as ship spoofing, hijacking, or availability disruption (e.g., frequency hopping, timing attacks, or slot starvation). [PERSON] et al. (2023) [PERSON] et al. (2023) proposed a blockchain-based architecture with a reputation voting mechanism for making the consensus process able to improve anti-attack capabilities. The proposed mutual authentication scheme was validated through simulation studies, which phase out the performance benefits in terms of data integrity and confidentiality. Furthermore, [PERSON] et al. (2018) [PERSON] et al. (2018) proposed a route plan exchange scheme based on a blockchain, pointing out the need for a distributed PKI for the port community actors to guarantee a secure information exchange mechanism.