Short-Term Performance Metrics Forecasting for Virtual Machine to Support Anomaly Detection Using Hybrid ARIMA-WNN Model

Qiu, Juan; Du, Qingfeng; Wang, Wei; Yin, Kanglin; Chen, Liang

doi:10.1109/compsac.2019.10228

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…e Temporal Hierarchical One-Class (THOC) network was proposed in [19]. Initially, it used a recurrent neural network to extract multi-scale temporal properties from time series.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

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MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

Reyana

Kautish²,

Yahia

et al. 2022

Computational Intelligence and Neuroscience

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Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network’s normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system’s efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness.

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“…e Temporal Hierarchical One-Class (THOC) network was proposed in [19]. Initially, it used a recurrent neural network to extract multi-scale temporal properties from time series.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

“…Utilizing sophisticated approaches for anomaly prediction in the future remains a challenge. The Temporal Hierarchical One-Class (THOC) network was proposed in [ 19 ]. Initially, it used a recurrent neural network to extract multi-scale temporal properties from time series.…”

Section: Related Workmentioning

confidence: 99%

MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

Reyana

Kautish²,

Yahia

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Before applying hybrid forecasting methods, the data can be divided into different components. "Wavelet transform" functions such as "discrete wavelet transform" (DWT) can be used [14][15][16] to divide the data into linear and nonlinear components [17]. Various evaluation methods can be used to measure the quality of forecast results.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Forecasting Methods—A Systematic Review

et al. 2023

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Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.

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A comparative study of series hybrid approaches to model and predict the vehicle operating states

Alizadeh

2021

Computers & Industrial Engineering

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Short-Term Performance Metrics Forecasting for Virtual Machine to Support Anomaly Detection Using Hybrid ARIMA-WNN Model

Cited by 7 publications

References 20 publications

MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

Hybrid Forecasting Methods—A Systematic Review

A comparative study of series hybrid approaches to model and predict the vehicle operating states

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