Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

Szmit, Maciej; Szmit, Anna

doi:10.1155/2012/192913

Cited by 38 publications

(15 citation statements)

References 16 publications

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“…Brutlag [1] and Evan Miller [15] using the Holt-Winters Forecasting algorithm. Szmit & Szmit summarize more applications of this algorithm for anomaly detection in network monitoring [28]. Sharifi et al show how neural networks can be used to predict failures in web applications [24].…”

Section: System Health Monitoringmentioning

confidence: 99%

The JDownloader Immune System for Continuous Deployment

Rechenmacher¹,

Riehle²,

Weber³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Continuous deployment can reduce the time from a source code change to a newly deployed application significantly. Increased innovation speed can make all the difference in a competitive market situation. However, deploying at high frequency requires high speeds of discovering bugs in the deployed software. Using the JDownloader file download manager as our example, we present a fitness model to evaluate a continuously deployed software during operation for expected behavior, present the design and implementation of a monitoring component, and evaluate the model and its implementation using data from JDownloader's multimillion member strong user base. Our evaluation finds that there had been thousands of undetected bugs, and that newly created bugs can be detected and reported 16 times faster than before.

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Section: System Health Monitoringmentioning

confidence: 99%

The JDownloader Immune System for Continuous Deployment

Rechenmacher¹,

Riehle²,

Weber³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…This technique is unsupervised but the computational burden, in our experience, is not negligible. To achieve full online anomaly detection capabilities, approaches frequently exploit statistical techniques that provide efficient training and analysis capabilites including changepoint detection [6], Holt-Winters method and its extensions [29], eccentricity analysis [5] This body of work addresses the problem of efficiently detecting anomalies, but contrarily to ADaaS they do not consider the problem of controlling the deployment and undeployment of the anomaly detectors. Indeed, ADaaS can incorporate different kinds of anomaly detectors, including statistical anomaly detectors as reported in this paper, whose deployment can be fully controlled by the operator.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection As-a-Service

Mobilio

Orrù

Riganelli

et al. 2019

2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

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Cloud systems are complex, large, and dynamic systems whose behavior must be continuously analyzed to timely detect misbehaviors and failures. Although there are solutions to flexibly monitor cloud systems, cost-effectively controlling the anomaly detection logic is still a challenge. In particular, cloud operators may need to quickly change the types of detected anomalies and the scope of anomaly detection, for instance based on observations. This kind of intervention still consists of a largely manual and inefficient ad-hoc effort.In this paper, we present Anomaly Detection as-a-Service (ADaaS), which uses the same as-a-service paradigm often exploited in cloud systems to declarative control the anomaly detection logic. Operators can use ADaaS to specify the set of indicators that must be analyzed and the types of anomalies that must be detected, without having to address any operational aspect. Early results with lightweight detectors show that the presented approach is a promising solution to deliver better control of the anomaly detection logic.

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“…In other words, recent observations are given relative more weight in forecasting than the older observations (Wang, 2010). A double exponential smoothing model applies the process of a simple exponential smoothing model to account for linear trend in time series data and a triple exponential smoothing model or Holt-Winters model can adjust for both trend and seasonality (Szmit and Szmit, 2012).…”

Section: Exponential Smoothing Modelsmentioning

confidence: 99%

“…The Holt-Winters exponential smoothing method was first introduced more than half a century ago for the trend and seasonal time series forecast and it is still one of the most popular forecasting systems widely used in many application areas (Szmit and Szmit, 2012). The autoregressive integrated moving average (ARIMA) model is usually used for time series data with trend and autoregression.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Models for Time Series With Covariates, Trend, Seasonality, Autoregression and Outliers

Tongkhow¹

2013

Journal of Computer Science

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Bayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the data analysis. The Gibbs sampling, a Markov Chain Monte Carlo (MCMC) algorithm, is used for the parameter estimation. The proposed models are applied to vegetable price time series data in Thailand. According to the RMSE, MAPE and MAE criteria for model comparisons, the proposed models provide the best results compared to the exponential smoothing models, SARIMA models and the Bayesian models with trend, auto regression and outliers.

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Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

Cited by 38 publications

References 16 publications

The JDownloader Immune System for Continuous Deployment

The JDownloader Immune System for Continuous Deployment

Anomaly Detection As-a-Service

Bayesian Models for Time Series With Covariates, Trend, Seasonality, Autoregression and Outliers

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