## 7 Conclusions and Future Work
* Thursday Morning: HD-DNCAE (SGD momentum) with OCSVM Sigmoid attained an AUC of 0.884, matching DNCAE's performance. * Friday BOT: HD-DNCAE (Adadelta) with IF reached AUC of 0.802 against 0.712 DNCAE. * Friday PortScan: HD-DNCAE (SGD momentum) with LOF outperformed with an AUC of 0.975 against DNCAE with 0.467. * Friday DDoS: HD-DNCAE (Adadelta) with LOF achieved an AUC of 0.947 outperforming DNCAE of 0.461. These results demonstrate HD-DNCAE's capability in detecting diverse anomalies, with notable improvements in specific scenarios, particularly when using the Adadelta optimiser. The performance across multiple datasets and classifiers establishes HD-DNCAE as a viable alternative in anomaly detection for cybersecurity applications, offering comparable or improved results in most cases. In the future, rigorous testing of both DNCAE and HD-DNCAE is expected to be conducted by simulating real-world attacks on the ICT stack of the seaport of Livorno. This activity will help us to determine how robust these algorithms are to never-before-seen attack scenarios, and, by undergoing this, we will be able to update the collected network dataset from the ICT stack to include not only normal data but other categories of attack. Conceptualization, M.T., A.T. and P.P. ; methodology, A.T., M.T., M.H. and K.G.G. ; software, M.H., K.G.G. and M.T. ; validation, M.H., K.G.G. and M.T. ; formal analysis, A.T., M.T. and P.P. ; investigation, M.T., A.T., M.H. and K.G.G. ; resources, M.T., M.H. and K.G.G. ; data curation, A.T. and M.T. ; writing--original draft preparation, M.T., A.T., M.H. and K.G.G. ; writing--review and editing, M.T., A.T., M.H., K.G.G. and P.P. ; visualization, A.T., M.T., M.H. and K.G.G. ; supervision, P.P. ; project administration, P.P. ; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. * [20] 5G-Assisted Maritime Autonomous Surface Ship--5G MASS. Available online: [[https://jlab-ports.cnit.it/5g-mass/](https://jlab-ports.cnit.it/5g-mass/)]([https://jlab-ports.cnit.it/5g-mass/](https://jlab-ports.cnit.it/5g-mass/)) (accessed on 20 June 2024). * [21] The NMEA Standard. Available online: [[https://www.nmea.org/](https://www.nmea.org/)]([https://www.nmea.org/](https://www.nmea.org/)) (accessed on 20 June 2024). * [22] IALA. Available online: [[https://www.iala-aism.org/about-iala/](https://www.iala-aism.org/about-iala/)]([https://www.iala-aism.org/about-iala/](https://www.iala-aism.org/about-iala/)) (accessed on 20 June 2024). * [23] S-100 Products. Available online: [[https://iho.int/en/standards-and-specifications](https://iho.int/en/standards-and-specifications)]([https://iho.int/en/standards-and-specifications](https://iho.int/en/standards-and-specifications)) (accessed on 20 June 2024). * [24] HDBSCAN Algorithm in Scikit-Learn. Available online: [[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html)) (accessed on 20 June 2024). * [25] Spring Boot. Available online: [[https://spring.io/projects/spring-boot](https://spring.io/projects/spring-boot)]([https://spring.io/projects/spring-boot](https://spring.io/projects/spring-boot)) (accessed on 20 June 2024). * [26] Spring Cloud Gateway. Available online: [[https://spring.io/projects/spring-cloud-gateway](https://spring.io/projects/spring-cloud-gateway)]([https://spring.io/projects/spring-cloud-gateway](https://spring.io/projects/spring-cloud-gateway)) (accessed on 20 June 2024). * [27] Open Source Identity and Access Management--Keycloak.