A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2105.13810v1)

Abstract: Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more complex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes.

• The paper demonstrates that LSTM networks effectively capture dynamic and contextual anomalies in complex technical systems.
• It evaluates encoder-decoder and hybrid LSTM architectures, emphasizing reconstruction errors and integrated approaches for enhanced detection.
• The study highlights emerging techniques like graph-based methods and transfer learning to further improve anomaly detection accuracy.

An Insightful Overview of Anomaly Detection in Technical Systems Using LSTM Networks

The preprint "A Survey on Anomaly Detection for Technical Systems using LSTM Networks" by Lindemann et al. emphasizes the increasing applicability of Long Short-Term Memory (LSTM) networks for anomaly detection in complex technical systems. As standard statistical and time-invariant methods often struggle with the dynamic and contextual nature of anomalies, the paper explores LSTM-based techniques as a promising alternative. It provides a comprehensive examination of LSTM models, discussing their architectural variations and evaluating their performance across different application domains.

LSTM Networks and Anomaly Detection

Anomalies, defined as deviations from expected system behavior, are prevalent in domains like manufacturing, medical systems, and network security. These deviations can lead to both efficiency loss and potential system failures. The complexities inherent in these systems necessitate robust methods for anomaly detection, particularly those capable of capturing temporal and contextual characteristics. LSTM networks, designed to mitigate the vanishing-gradient problem of traditional RNNs, are suited to learn long-term dependencies, making them apt for detecting anomalies characterized by intricate temporal dynamics.

Regular LSTM architectures have been effectively utilized to identify collective and contextual anomalies by leveraging their ability to handle multivariate time series and temporal dependencies. For example, stacked LSTM networks model and predict system dynamics, detecting anomalies based on deviations from predicted outputs.

Encoder-Decoder and Hybrid Approaches

The research highlights encoder-decoder-based LSTM architectures, which include Autoencoders (AEs) and Sequence-to-Sequence (Seq2Seq) models, tailored for unsupervised anomaly detection in high-dimensional data scenarios. These models effectively learn compressed representations of normal system behavior, with anomalies manifesting as reconstruction errors. Variational AEs and robust deep AEs further enhance detection by incorporating probabilistic elements and noise reduction, respectively.

Hybrid models combine LSTM networks with other algorithms, like Support Vector Machines (SVMs) or Generative Adversarial Networks (GANs), to refine anomaly detection capabilities. This dual approach—with one component predicting normal system behavior and the other identifying deviations—confers a robustness essential for dealing with diverse anomaly types, such as global and local anomalies.

Emerging Techniques: Graph-Based and Transfer Learning Approaches

The survey also explores emerging trends like graph-based anomaly detection. Graphs provide a rich framework for encapsulating relational data, enabling the detection of both collective and contextual anomalies through clustering and node analysis. Despite their potential, practical implementation challenges such as data structure complexity and context profiling remain significant barriers.

Transfer learning offers another promising avenue by addressing the data scarcity often encountered in anomaly detection applications. It facilitates knowledge transfer from pre-trained models on unrelated datasets to new tasks, thus enhancing detection accuracy without extensive retraining. Although relatively nascent, these approaches show promise in improving the adaptability and generalization of anomaly detection systems.

Implications and Future Directions

The paper's survey underscores LSTM networks as a core component of modern anomaly detection strategies, particularly in handling time-variant and context-dependent scenarios. Despite their established efficacy, the integration of graph-based representations and transfer learning presents new opportunities for advancing detection accuracy and efficiency.

The paper invites further exploration into combining LSTM networks with these emerging methodologies, aiming for comprehensive frameworks capable of detecting anomalies in highly networked systems. Such developments would address pivotal challenges in fields like autonomous systems and industrial automation, where discerning between anomalous and normal adaptive behaviors remains a critical concern.

Overall, this paper furnishes a detailed exploration of state-of-the-art anomaly detection using LSTM networks, providing a solid foundation for future research endeavors in this dynamic field.