Anomaly detection by using multimodal deep learning

Guangxin Jiang; Zhenyu Wan; Kunhan Wang; Jinsi Han; Shengli Wu; Min Tong

doi:10.1117/12.3011769

7 December 2023 Anomaly detection by using multimodal deep learning

Guangxin Jiang, Zhenyu Wan, Kunhan Wang, Jinsi Han, Shengli Wu, Min Tong

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129414V (2023) https://doi.org/10.1117/12.3011769
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In the field of power systems, the detection of multidimensional fault data is essential. Introducing the concept of IT Operations Artificial Intelligence (AIOps), combined with big data and machine learning techniques, aims to take place extensive IT operational tasks, including service availability and performance monitoring. Multiple models are utilized to process monitoring data patterns, such as text attributes and real-value response times extracted from logs and traces, enabling the detection of faults and potential anomalies in cloud services. To detect anomalies in the execution of system components, a dual-mode distributed tracing data from large-scale cloud infrastructure is employed, and a novel method for anomaly detection is proposed. The application of LSTM (Long Short-Term Memory) multimodal neural networks is demonstrated to learn the sequential characteristics of the two modes of data in traces. The capability to detect dependencies and concurrent events is showcased through a method that reconstructs execution paths using the proposed models. In experimental evaluations using large-scale production cloud data, the new approach outperforms traditional architectures and other deep learning methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangxin Jiang, Zhenyu Wan, Kunhan Wang, Jinsi Han, Shengli Wu, and Min Tong "Anomaly detection by using multimodal deep learning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129414V (7 December 2023); https://doi.org/10.1117/12.3011769

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