A task of anomaly detection for a smart satellite Internet of things system
<|reference_start|>A task of anomaly detection for a smart satellite Internet of things system: 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.<|reference_end|>
@article{shao2024a,
    title={A task of anomaly detection for a smart satellite Internet of things
  system},
    author={Zilong Shao},
    journal={arXiv preprint arXiv:2403.14738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14738},
    primaryClass={cs.LG eess.SP}
}