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- W4381744889 abstract "The Industrial Internet of Things (IIoT) applications is usually associated with stringent latency requirements. An anomaly in an IIoT edge network deteriorates the performance and thus needs a real-time Root Cause Analysis (RCA) to identify the anomalous node and provide robust network infrastructure. In this paper, we present an automated, real-time RCA technique to identify the network-level root cause nodes. We use a deep learning-based approach, exploiting a Graph Neural Network (GNN) to identify root cause nodes. In GNN-RCA, we used a sampling technique and optimized aggregator function to reduce detection time. We have shown that the proposed GNNRCA method outperforms the existing benchmarks in terms of classification score and execution time." @default.
- W4381744889 created "2023-06-24" @default.
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- W4381744889 date "2023-05-08" @default.
- W4381744889 modified "2023-09-26" @default.
- W4381744889 title "A Deep Learning Approach for Root Cause Analysis in Real-Time IIoT Edge Networks" @default.
- W4381744889 doi "https://doi.org/10.1109/noms56928.2023.10154218" @default.
- W4381744889 hasPublicationYear "2023" @default.
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