End-to-end deep learning for smart maritime threat detection: an AE–CNN–LSTM-based approach

Smart maritime operations face growing cyber risks due to the proliferation of IoT-enabled sensors, navigation units, and communication links. To improve detection fidelity under these conditions, we present a hybrid Autoencoder–Convolutional Neural Network–Long Short-Term Memory (AE–CNN–LSTM) based...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 36316 - 26
Main Authors: Anuja, R., Annrose, J.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 17.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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Autoencoder (AE) models
Benchmarks
Blockchain
Connectivity
Convolutional neural networks (CNNs)
Datasets
Deep learning
Deep learning techniques
Design
False alarms
Feature selection
Firmware
Humanities and Social Sciences
Internet of Things
IoT-based intrusion detection systems
Long short-term memory
Long short-term memory (LSTM) networks
Maritime cybersecurity
multidisciplinary
Neural networks
Ports
Privacy
Science
Science (multidisciplinary)
Security systems
Sensors
Autoencoder (AE) models
Long short-term memory (LSTM) networks
Convolutional neural networks (CNNs)
Maritime cybersecurity
IoT-based intrusion detection systems
Deep learning techniques
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Smart maritime operations face growing cyber risks due to the proliferation of IoT-enabled sensors, navigation units, and communication links. To improve detection fidelity under these conditions, we present a hybrid Autoencoder–Convolutional Neural Network–Long Short-Term Memory (AE–CNN–LSTM) based framework that unifies unsupervised reconstruction signals with spatio-temporal feature learning for intrusion detection in marine cyber-physical networks. The model is trained and evaluated on a KDDCup99-based benchmark adapted to simulated maritime scenarios and supports both binary and multiclass classification. In the binary setting, the system attains 99.8% accuracy; in the multiclass setting it demonstrates consistently strong performance across precision, recall, F1-score, and AUC, with minority-class behavior analyzed via confusion matrices and threshold sensitivity. Reconstruction errors (MAE/MSE) provide an auxiliary anomaly cue that aids triage. In this study the results are compared with representative deep-learning and transformer baselines, the proposed model yields competitive to superior results while remaining suitable for real-time deployment in smart ports, autonomous vessels, and underwater sensor networks. We also discuss practical constraints—such as dataset realism and class imbalance-to contextualize applicability in operational environments.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-19450-4