Liu, Z.; Ling, M.; Zhu, T., and Xu, D., 2020. Deep neural network in safety analysis of maritime exploration equipment. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 155–158. Coconut Creek (Florida), ISSN 0749-0208.
Safety analysis in maritime geological exploration is an alternative attack that exploits leaking information from physical detecting implementations in maritime detecting domains. Many studies investigated deep learning tools to improve profiled attacks against leaking data, Multilayer Perceptron Model and Convolutional Neural Network Model. Compared with traditional correlation attacks, using neural network assistants to cope with traces misalignment and the low signal-noise-ratio leaking data more useful. However, the efficiency of attacks depends to all hyperparameters adjusted to optimize efficiency and neural network structures with numbers. In this paper, the research utilizes different processing methods to demonstrate its efficiency with kinds of neural networks.