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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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 36316 - 26
Hlavní autori:	Anuja, R., Annrose, J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 17.10.2025 Nature Publishing Group Nature Portfolio
Predmet:	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
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Abstract	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.
AbstractList	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. Abstract 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. 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.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.
ArticleNumber	36316
Author	Anuja, R. Annrose, J.
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Cites_doi	10.1093/comjnl/bxaf065 10.3390/s24113458 10.3390/s25103043 10.1016/j.knosys.2023.110941 10.1007/s10922-025-09963-8 10.1007/s10207-023-00876-0 10.1016/j.jnca.2015.11.016 10.1007/s10115-025-02041-0 10.1109/ICCBD-AI65562.2024.00030 10.58346/JISIS.2025.I2.057 10.14445/23488549/IJECE-V11I6P109 10.1109/ISNCC62547.2024.10758956 10.3390/s22103744 10.2991/978-94-6463-762-3_17 10.1109/GLOBECOM54140.2023.10437860 10.3390/electronics13020276 10.3390/s22041396 10.33022/IJCS.V13I1.3720 10.1016/j.eswa.2024.123808 10.1109/TCE.2024.3499942 10.30574/wjarr.2024.21.3.0983 10.1038/s41598-025-85248-z 10.1016/j.jnca.2021.103271 10.1109/JBHI.2024.3352013 10.3390/jmse11010221 10.3390/electronics12204289 10.1007/s12083-025-01944-7 10.1016/j.inffus.2023.102002 10.1038/s41598-025-87454-1 10.3390/app15031552 10.1007/978-981-99-7139-2_18 10.3934/nhm.2025024
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SubjectTerms	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
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Title	End-to-end deep learning for smart maritime threat detection: an AE–CNN–LSTM-based approach
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