A hybrid approach of ConvLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge-cloud environments

Anomaly detection is a critical requirement across diverse domains to promptly identify abnormal behavior. Conventional approaches often face limitations with uninterpretable anomaly detection results, impeding efficient decision-making processes. This paper introduces a novel hybrid approach, the convolutional LSTM Bayesian neural network with nonparametric dynamic thresholding (ConvLSTMBNN-DT) for prediction-based anomaly detection. In addition, the model integrates fine-tuned generative pre-training version 4 (GPT-4) to provide human-interpretable explanations in edge-cloud environments. The proposed method demonstrates exceptional performance, achieving an average F 1 - score of 0.91 and an area under the receiver operating characteristic curve (AUC) AUC ) of 0.86. Additionally, it effectively offers comprehensible decision-support explanations. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).