Application of Time Series Analysis in Predicting Industrial Data Security Events

In today's industrial operations, the widespread application of digital technology has made industrial data security an increasingly important focus. Industrial data encompasses vital information in key areas such as production, manufacturing, and supply chains, making its security crucial for the stability of the entire operational system. However, with the rapid growth of industrial data, traditional security measures have proven to be inadequate. Therefore, this paper discusses how to use time series analysis to predict industrial data security incidents, thereby safeguarding the security of industrial data. Time series analysis, as an effective tool, can help identify patterns and trends in historical data, which can be utilized to forecast potential security incidents. This paper will demonstrate the effectiveness of time series analysis in the field of industrial data security and propose a security event prediction method based on historical data patterns. Through this research, we aim to provide more theoretical support and practical guidance for the field of industrial data security. This paper establishes a multi-scale temporal convolutional neural network for predicting industrial data security events. Firstly, in the temporal convolutional network, multi-scale modules are embedded, and two different-structured deep learning blocks are constructed. By changing the dilation factor in the dilated convolution, multi-scale modules are embedded to learn feature information at different scales and to integrate them, thereby achieving feature learning and prediction of industrial data security events at different scales. Secondly, residual connections are introduced to prevent overfitting and underfitting phenomena.