B-Detection: Runtime Reliability Anomaly Detection for MEC Services With Boosting LSTM Autoencoder

Wang, Lei; Chen, Shuhan; Chen, Feifei; He, Qiang; Liu, Jiyuan

doi:10.1109/tmc.2023.3262233

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…When the dataset is too large, its performance degrades. B-Detection [73] is primarily used to detect runtime reliability anomalies in MEC services. It uses LSTM and AE models to capture the normal reliability data stream distribution characteristics.…”

Section: Concept Drift Adaptation Methods Based On Hybrid Learningmentioning

confidence: 99%

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Xiang

Cong

et al. 2023

Applied Sciences

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With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

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Section: Concept Drift Adaptation Methods Based On Hybrid Learningmentioning

confidence: 99%

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Xiang

Cong

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

Enhancing Driving Behavior Analysis in Autonomous Systems: A Reservoir Computing and Temporal-Aware Machine Learning Approach

Nowshin,

Sethi,

Dong

et al. 2024

2024 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)

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Runtime Anomaly Detection for MEC Services with Multi-Timescale and Dimensional Feature

Yang,

Li,

Chen

et al. 2024

2024 IEEE Wireless Communications and Networking Conference (WCNC)

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B-Detection: Runtime Reliability Anomaly Detection for MEC Services With Boosting LSTM Autoencoder

Cited by 3 publications

References 44 publications

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Enhancing Driving Behavior Analysis in Autonomous Systems: A Reservoir Computing and Temporal-Aware Machine Learning Approach

Runtime Anomaly Detection for MEC Services with Multi-Timescale and Dimensional Feature

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