Research on Data Outbound Risk Assessment Algorithm for the Automobile Industry Based on Deep Learning

This paper proposes a data outbound risk assessment algorithm for the automobile industry based on deep learning, aiming to achieve efficient and accurate assessment of data outbound risks through the Transformer architecture and multimodal data fusion strategy. The algorithm first classifies and extracts features of automotive industry data, and constructs a risk assessment system that includes indicators such as data sensitivity, data volume, data flow, and storage security. Subsequently, by utilizing the powerful parallel computing capabilities and long-distance dependency modeling capabilities of the Transformer architecture, combined with the multimodal data fusion strategy, different types of data (such as text, images, and numerical values) are integrated into the model to achieve accurate prediction of risks. The experimental results show that the precision of this algorithm is 92.3 %, which is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 5. 6}$</tex> percent higher than the conventional SVM, DT, etc., and the convergence rate is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 2. 4 \%}$</tex>. Furthermore, by introducing the attention mechanism, this algorithm is able to identify the critical risk factors efficiently, and provide an interpretability for risk evaluation. For example, it was found in the experiment that data sensitivity and data flow are two key factors affecting the risk level. This study provides new technical means for the security of data outbound transfer in the automotive industry, which has important theoretical and application value.