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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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 36316 - 26
Hauptverfasser:	Anuja, R., Annrose, J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 17.10.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	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
Online-Zugang:	Volltext
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