  When the equipment is working, real-time collection of environmental sensor
data for anomaly detection is one of the key links to prevent industrial
process accidents and network attacks and ensure system security. However,
under the environment with specific real-time requirements, the anomaly
detection for environmental sensors still faces the following difficulties: (1)
The complex nonlinear correlation characteristics between environmental sensor
data variables lack effective expression methods, and the distribution between
the data is difficult to be captured. (2) it is difficult to ensure the
real-time monitoring requirements by using complex machine learning models, and
the equipment cost is too high. (3) Too little sample data leads to less
labeled data in supervised learning. This paper proposes an unsupervised deep
learning anomaly detection system. Based on the generative adversarial network
and self-attention mechanism, considering the different feature information
contained in the local subsequences, it automatically learns the complex linear
and nonlinear dependencies between environmental sensor variables, and uses the
anomaly score calculation method combining reconstruction error and
discrimination error. It can monitor the abnormal points of real sensor data
with high real-time performance and can run on the intelligent satellite
Internet of things system, which is suitable for the real working environment.
Anomaly detection outperforms baseline methods in most cases and has good
interpretability, which can be used to prevent industrial accidents and
cyber-attacks for monitoring environmental sensors.
