Detection and Security Protection of Abnormal Traffic in underwater Sensor Networks based on Deep Learning and Bio-inspired Algorithms

Lin Li
The 760th Research Institute China State Shipbuilding Corporation Limited, Dalian 116013, Liaoning, China

Abstract:

This paper focuses on the detection of anomalous traffic and security protection in underwater sensor networks. Underwater sensor networks play a crucial role in marine development, but face severe challenges from anomalous traffic, which can stem from malicious attacks, equipment failures, and environmental interference, all of which can seriously affect network performance and security. To address this issue, this study introduces deep learning technology, combined with bio-inspired algorithms such as the ant colony algorithm, to optimize the traffic detection and security protection strategies. The paper elaborates on the basic theory of deep learning, analyzes its advantages in anomalous traffic detection, and introduces applicable algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The bio-inspired optimization method based on the ant colony algorithm is used to enhance the performance of deep learning models, improving their adaptability and robustness in complex and dynamic environments. Additionally, a strategy for anomalous traffic detection based on deep learning and bio-inspired algorithms is proposed, covering data preprocessing, detection methods, and optimization strategies. Moreover, a security protection system for underwater sensor networks is constructed, which includes security response mechanisms, encryption, and authentication technologies. The results show that deep learning technology can effectively improve the accuracy of anomalous traffic detection, and the integration of bio-inspired algorithms significantly enhances the model's performance. This system substantially strengthens network security and provides a more stable network environment for marine monitoring and research.