###### Abstract
Therefore, a combination of the Deep Autoencoder and Hierarchical Density-Based Spatial Clustering (HD-DNCAE [3]) ML-based algorithms was developed to provide the digital infrastructure with both anomalies and system intrusion detection capabilities, conforming with the zero-touch approach. The ICT Prototyping Framework [4] was adopted and used as a reference architecture and framework for the development and deployment of the components of the autonomous shipping application used for the experimental validation of the proposed approach. The first challenge was to implement the SECOM standard to secure the communication channel by adopting a microservice architecture. The use of an ICT framework with a layered structure made possible this approach, which is a novelty in the maritime landscape. Employing a microservice architecture enables the possibility to address the scalability and fault-tolerance aspects, which are crucial in the autonomous shipping context. Furthermore, it has been challenging to develop an anomaly and intrusion detection ML-based algorithm that takes into account misbehaviour coming from the on-shore infrastructure (e.g., anomalous readings from a sensor deployed in the port area or an intrusion on the land-side infrastructure). It is normal to think that systems on board the ship are not designed with cybersecurity as a consideration because of their \"air-gapped by default\" status [5] and so they are more exposed to cyberattacks, but in the autonomous shipping landscape it is also fundamental to consider the cyber-resilience to attacks for the land-side infrastructure that may be devoted to the remote monitoring and control of the ship during the manoeuvres. In addition, the proposed model is very flexible, as it does not require defining the number of clusters in advance but can learn how to classify data on its own. Therefore, the contribution and novelty of this paper can be summarized in two main points:  
* The implementation of the SECOM standard using a microservice-based approach allowed us to ensure end-to-end data integrity, confidentiality (data encryption), non-repudiation (employing private keys), and authentication mechanisms (for data and services through JSON Web Tokens--JWT) by establishing a secure communication channel between the ship and the SCC to mitigate traditional cybersecurity vulnerabilities of the maritime communication systems (e.g., man-in-the-middle and distributed denial-of-service);
* The implementation of the HD-DNCAE deep learning algorithm on top of the ICT infrastructure of the Livorno seaport allowed us to detect known and novel cyberattacks on the existing digital infrastructure without predefining the number of clusters and to provide an optimal Area Under the Curve (AUC) score leading to a better clustering of data. The rest of this paper is organised as follows: Section 2 addresses relevant related works; Section 3 describes the reference architecture used for the application development as well as the application itself; Section 4 is focused on the SECOM standard by describing how it was applied to the considered scenario; Section 5 describes ML algorithms and how they were applied to detect anomalies and intrusions within the underlying digital infrastructure; in Section 6 experimental setup and performance evaluation are described; finally, Section 7 presents a discussion of the main findings of the paper and future works.