A novel deep learning approach for intrusion detection in maritime radar networks
Md Mostofa Nurannabi Shakil, Md. Alamgir Hossain, Md. Samiul Islam, Nidadavolu Venkat Durga Sai Siva Vara Prasad Raju · 2025
Read A novel deep learning approach for intrusion detection in maritime radar networks by Md Mostofa Nurannabi Shakil, Md. Alamgir Hossain, Md. Samiul Islam, Nidadavolu Venkat Durga Sai Siva Vara Prasad Raju (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12858934.txt.
Description
Md Mostofa Nurannabi Shakil and co-authors build a deep learning guard for maritime radar networks, in a 2025 paper. Linked radar systems keep shipping safe, but their connectivity invites cyber intrusion, and older detectors lean on hand-made features and shallow models that miss evolving attacks.
Their system, MARINERNet, stacks one-dimensional convolutions with squeeze-and-excitation blocks and residual links, learning features straight from raw network traffic. It sorts connections as normal or as attack types. In testing it classified attack categories with about ninety-nine percent accuracy and flagged anomalies perfectly on the binary task. The authors pitch it as scalable and fast enough for real-time watch on critical sea infrastructure.
Catalog record
| CREATOR | Md Mostofa Nurannabi Shakil, Md. Alamgir Hossain, Md. Samiul Islam, Nidadavolu Venkat Durga Sai Siva Vara Prasad Raju |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 99 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |