A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

Yan, Peng; Abdulkadir, Ahmed; Luley, Paul-Philipp; Rosenthal, Matthias; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo

doi:10.1109/access.2023.3349132

Cited by 32 publications

(3 citation statements)

References 125 publications

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“…Based on the above problem statement and the various TL for TSF shortcomings discussed in [10,11], we define the following research questions (RQs):…”

Section: Problem Statement and Research Questionsmentioning

confidence: 99%

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Kambale,

Salem,

Benarbia

et al. 2024

Symmetry

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Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.

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“…Based on the above problem statement and the various TL for TSF shortcomings discussed in [10,11], we define the following research questions (RQs):…”

Section: Problem Statement and Research Questionsmentioning

confidence: 99%

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Kambale,

Salem,

Benarbia

et al. 2024

Symmetry

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“…Hybrid approaches in anomaly detection combine multiple techniques to leverage the strengths of each method, thereby enhancing detection accuracy and reducing false positives. These approaches integrate different algorithms and models to create a more robust system capable of handling diverse data patterns and evolving threats [75]. The synergy between various methods helps mitigate the weaknesses inherent in individual techniques, resulting in a more comprehensive and effective anomaly detection solution.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud

Olateju,

Okon,

Igwenagu

et al. 2024

Asian J. Res. Com. Sci.

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Anomaly detection is critical for network security, fraud detection, and system health monitoring applications. Traditional methods like statistical approaches and distance-based techniques often struggle with high-dimensional and complex data, leading to high false positive rates. This study addresses the challenge by investigating advanced AI-driven techniques to reduce false positives and enhance data security within cloud computing environments. This study employs deep learning models, integrates contextual data, and incorporates comprehensive security measures to enhance anomaly detection performance. Data from synthetic sources, such as the NSL-KDD dataset and real-world cloud environments, were utilized to capture user behavior logs, system states, and network traffic. Over 50 academic journals were reviewed, and 21 were selected based on inclusion criteria, such as relevance to AI-driven anomaly detection, empirical performance metrics, and the focus on cloud environments, and exclusion criteria that filtered out studies lacking empirical data or not specific to cloud-based systems. Methodologically, the research involves a comparative analysis of different AI techniques and their impact on false positive rates, accuracy, precision, and recall. The findings demonstrate that deep learning techniques significantly outperform traditional methods, achieving a lower false positive rate and higher accuracy. The results underscore the importance of contextual data and robust security protocols in reliable anomaly detection. This research fills a gap by thoroughly evaluating advanced AI techniques for reducing false positives in cloud environments. The study's significance lies in guiding the development of more effective anomaly detection systems, thereby enhancing security and reliability across various applications. Additionally, organizations should invest in continuously developing and integrating AI-driven anomaly detection systems with comprehensive security measures to improve their effectiveness the study suggests that further study be conducted with large datasets to evaluate the effectiveness of Hybrid anomaly detection systems in detecting and addressing false positives.

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“…Reference source not found., anomaly detection in electrocardiogram (ECG) time series data stands out as a critical application 4. The typical workflow of time series anomaly detection generally involves the following steps 5: (1) partitioning the available time series dataset into training and testing sets; (2) selecting appropriate feature extraction methods; (3) establishing anomaly detection models to identify abnormal patterns; (4) conducting anomaly detection and evaluation on the testing set.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data

Wu,

Ali,

et al. 2024

Preprint

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The anomaly detection of electrocardiogram (ECG) data is crucial for identifying deviations from normal heart rhythm patterns and providing timely interventions for high-risk patients. Various autoencoder (AE) models within machine learning (ML) have been proposed for this task. However, these models often do not explicitly consider the specific patterns in ECG time series, thereby impacting their learning efficiency. In contrast, we adopt a method based on prior knowledge of ECG time series shapes, employing multi-stage preprocessing, adaptive convolution kernels, and Toeplitz matrices to replace the encoding part of the AE. This approach combines inherent ECG features with the symmetry of Toeplitz matrices, effectively extracting features from ECG signals and reducing dimensionality. Our model consistently outperforms state-of-the-art models in anomaly detection, achieving an overall accuracy exceeding 99.6%, with Precision and Area Under the Receiver Operating Characteristic Curve (AUC) reaching 99.8%, and Recall peaking at 99.9%. Moreover, the runtime is significantly reduced. These results demonstrate that our technique effectively detects anomalies through automatic feature extraction and enhances detection performance on the ECG5000 dataset, a benchmark collection of heartbeat signals.

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A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

Cited by 32 publications

References 125 publications

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud

Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data

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