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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 15; číslo 1; s. 36316 - 26
Hlavní autoři: Anuja, R., Annrose, J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 17.10.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/4077
639/705
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-19450-4