A Comprehensive Analysis of Security-Based Schemes in Underwater Wireless Sensor Networks

Saeed, Khalid; Khalil, Wajeeha; Al-Shamayleh, Ahmad Sami; Ahmed, Sheeraz; Akhunzada, Adnan; Alharthi, Salman Z.; Gani, Abdullah

doi:10.3390/su15097198

Cited by 3 publications

(1 citation statement)

References 90 publications

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“…Navigators can plan routes that avoid bad weather conditions since wireless connectivity makes it easier to receive weather reports on time. With the ability to provide precise and current meteorological information, this capability is required for minimizing weather-related mishaps and maximizing maritime safety [13], [14]. Maintaining communication system cybersecurity has significant importance because maritime communication is growing more digital and depends on wireless technologies.…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications

Algarni,

Acarer,

Ahmad

2024

IEEE Access

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The safety of maritime environments in context with effective and secure wireless communication networks is required for ships, coastal stations, and maritime authorities. The dynamic nature of marine environments, where ships traverse vast and unpredictable expanses of oceans and seas, presents big challenges to safety and risk management. Wireless communication technology is widely employed in maritime activities for communication via ocean networks and underwater wireless sensor networks (UWSNs). Maintaining the safety of the maritime environment, effective anomaly detection, prompt risk mitigation, and real-time communication becomes more difficult due to its dynamic nature. International trade and transportation are facilitated by the maritime industry. In addition to protecting lives and averting environmental disasters, maritime safety is important for maintaining the effectiveness and dependability of shipping routes. To handle the intricacies of maritime safety, this work proposes a novel preventive framework for anomaly detection and risk management in Maritime Wireless Communications (MWC). The proposed framework is based on edge computing and machine learning models. The framework makes use of edge computing technology to process data locally, lowering latency and enabling real-time communication in maritime environments. A proactive safety approach has been adopted to ensure the wellbeing of seafarers, safeguard vessels, and protect the marine environment. As maritime cybersecurity threats continue to evolve, the proposed research aims to enhance the cybersecurity posture of MWC. The framework will incorporate measures to detect and respond to potential cyber threats, ensuring the integrity and security of communication channels under international maritime cybersecurity standards. The proposed anomaly detection framework incorporates machine learning models such as Long Short-Term Memory (LSTM) and Isolation Forests (IF). The proposed framework also places a strong emphasis on preventative safety measures, including cybersecurity safeguards to protect communication channels in the constantly changing digital marine operations environment. To demonstrate the effectiveness of the proposed framework, the experiments were performed based on a publicly available dataset and implemented in the context of marine communications. The results show significant accuracy as well as high precision, recall, and F1-score metrics generated by the LSTM and IF models. The results highlight that the proposed framework can detect anomalies and potential threats in real-time marine communications.INDEX TERMS Machine learning; intelligent systems; sustainable navigation, autonomous vessels, ship safety management systems; maritime shipping and satellite technology; sensing and communication in maritime; automatic identification system; edge computing, prevention of ship accidents.

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Section: Introductionmentioning

confidence: 99%

An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications

Algarni,

Acarer,

Ahmad

2024

IEEE Access

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Localization algorithm based on deep neural network and improved COA optimization

yu,

ye,

liu

et al. 2024

Preprint

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To improve the positioning accuracy of range-independent positioning algorithms in wireless sensor networks, a DV-Hop localization algorithm (DDCO) based on deep learning and improved crayfish optimization was proposed. Firstly, the dual communication radius subdivision of the minimum number of hops is introduced to reduce the error due to the number of hops; then the trained deep neural network model is used to correct the estimated distance to reduce the distance estimation error; finally, the random center of gravity inverse learning and the improved crayfish algorithm with nonlinear function are introduced to calculate the coordinates of the unknown nodes, and the global optimization capability of the intelligent algorithm is used to reduce the error generated by the DNN. The simulation results show that the positioning error of the DDCO algorithm is reduced by 54.4%, 23.4%, and 10.5%, respectively, compared with DV-Hop and other comparison algorithms under different communication radii. Under different beacon node densities, the error decreases by 46.2%, 24.2%, and 10.6%, respectively. Under different node densities, the error decreases by 49.6%, 30.3%, and 17.3%, respectively.

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GTR: GAN-Based Trusted Routing Algorithm for Underwater Wireless Sensor Networks

Wang,

Ben

2024

Sensors

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The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%.

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A Comprehensive Analysis of Security-Based Schemes in Underwater Wireless Sensor Networks

Cited by 3 publications

References 90 publications

An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications

An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications

Localization algorithm based on deep neural network and improved COA optimization

GTR: GAN-Based Trusted Routing Algorithm for Underwater Wireless Sensor Networks

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