Deep generation network for multivariate spatio-temporal data based on separated attention

Lin, Lianlei; Wang, Junkai; Gao, Sheng; Zhang, Zongwei

doi:10.1016/j.ins.2023.03.062

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Moreover, multivariate time series prediction [12][13][14] is a sophisticated analytical approach that involves forecasting future values of multiple interrelated variables over time. Unlike univariate time series analysis, which focuses on a single variable, multivariate time series prediction considers the dynamic interactions and dependencies among several variables simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Psychogyios,

Papadakis,

Bourou

et al. 2024

Future Internet

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The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Psychogyios,

Papadakis,

Bourou

et al. 2024

Future Internet

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“…The application of self-attention is a good solution to these problems. Transformers were first proposed for solving natural language processing (NLP) problems, and they have since been widely used in the computer vision [24]- [28], remote sensing [29]- [31], and temporal prediction fields [32], [33], among others. [32] proposed Informers based on Transformers, effectively replacing traditional self-attention with ProbSpare self-attention and greatly reducing the computational effort.…”

Section: Introductionmentioning

confidence: 99%

StHCFormer: A Multivariate Ocean Weather Predicting Method Based on Spatiotemporal Hybrid Convolutional Attention Networks

Lin,

Zhang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ocean weather prediction is crucial for various applications, such as global climate prediction, marine environmental protection, and offshore production. However, current data-based marine weather prediction methods have limitations when predicting multiple variables in a particular area, failing to meet the efficiency and accuracy requirements of practical applications. In the realm of ocean weather variations, the presence of highly interconnected spatial and temporal continuations, coupled with the mutual influence of individual variables, underscores the utmost importance of effectively capturing dynamic correlations encompassing space, time, and variables to accurately predict ocean weather. To address this, we developed a novel approach called StHCFormer, which is a multivariate spatiotemporal hybrid convolutional attention network. The first key component of StHCFormer is the spatiotemporal hybrid convolutional attention (StHCA) module, which leverages a hybrid convolutional attention mechanism to explore both global spatial representations and local features. Additionally, the module incorporates temporal attention to capture the temporal dependence of weather records and effectively captures the dynamic correlations among multiple variables through channel deflation and weighted residuals. To ensure balanced variable losses, we introduced the concept of homoscedasticity uncertainty loss to dynamically adjust the multitask weights. This guarantees a global optimal solution and leads to more accurate multivariate ocean weather prediction. Finally, we conducted a comprehensive evaluation and comparison of the StHCFormer model with other state-of-the-art algorithms using the ERA5 dataset in the Philippine Sea. The results demonstrated that StHCFormer outperforms existing methods in marine multivariate field weather prediction.

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STCM: A spatio-temporal calibration model for low-cost air monitoring sensors

Zhang

Qin

et al. 2023

Information Sciences

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Deep generation network for multivariate spatio-temporal data based on separated attention

Cited by 7 publications

References 37 publications

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

StHCFormer: A Multivariate Ocean Weather Predicting Method Based on Spatiotemporal Hybrid Convolutional Attention Networks

STCM: A spatio-temporal calibration model for low-cost air monitoring sensors

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