Man-Made Threat Event Recognition Based on Distributed Optical Fiber Vibration Sensing and SE-WaveNet

Real-time identification of threats has been the focus of attention in the security field in recent years. Researchers have aimed to build a fast, efficient, and accurate model to address risks in threat event identification. This study proposes a fast, effective deep learning model-the squeeze and excitation WaveNet (SE-WaveNet). WaveNet is a 1-D convolutional neural network (CNN) model with a large receptive field. It is small, fast, and highly accurate. Thus, it has a great advantage in processing time series data. On this basis, the squeeze and excitation (SE) structure of the attention mechanism is introduced to adapt to matrix data with complete information. To ensure the integrity of information in the experimental verification, the distributed optical fiber vibration sensing (DVS) system is used to collect the data near the vibration point and form the time-space matrix as the input of the model. SE-WaveNet is compared with WaveNet and related models. Results show that the accuracy of SE-WaveNet based on WaveNet is increased by approximately 3.5%, the model size is reduced by 39%, and the speed is increased by 32%. Compared with the five other related models, the average performance is 3%-9% higher, and the final test accuracy rate can reach approximately 97.73%.

Automated summary

Real-time threat identification has been a focal point in the security field, with researchers developing the SE-WaveNet deep learning model to efficiently address risks in threat event identification. By introducing the SE structure of the attention mechanism, the SE-WaveNet adapts to matrix data with complete information. Experimental results show that SE-WaveNet outperforms other models in accuracy, model size reduction, and processing speed.