On Periodicity Detection and Structural Periodic Similarity

Vlachos, Michail; Yu, Philip S.; Castelli, Vittorio

doi:10.1137/1.9781611972757.40

Cited by 202 publications

(127 citation statements)

References 15 publications

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“…Since period and frequency are the reverse of each other, dominant periods can be identified by finding frequencies which carry most of the energy. Periodogram and circular autocorrelation are two popular estimators that are used to find PSD [17]. Both methods are calculated using Discrete Fourier Transform (DFT) [18].…”

Section: Periodicitymentioning

confidence: 99%

“…Low accuracy in detection of large period components and spectral leakage are two main issues associated with this estimator [17]. The second way to find dominant periods in a time series is to estimate circular autocorrelation function(ACF):…”

Section: Periodicitymentioning

confidence: 99%

“…ACF can address the problems related to periodogram estimator. This approach which will be used in this paper, is introduced in [17]. First we find candidate periods by using periodogram estimator.…”

Section: Periodicitymentioning

confidence: 99%

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Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

Kayatas

Fouladi

Ermis

et al. 2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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Data availability should be guaranteed by a web service in order to satisfy customers. One of the main challenges of information security professionals is DDoS attack which affects the availability. By masquerading itself as a legitimate user, a DDoS attacker tries to overwhelm a server by sending a great number of useless packets that influences the quality of service (QoS) of the network. DDoS attack can result in a great damage to network services. Useless packets similar to normal ones are dispatched by the attacker which leaves the intrusion detection system impotent of detection. Transferring from conventional packet-based analysis methods to time series based (flow-based) algorithms would be a promising alternative to spot DDoS attacks. In this work, we extract four measures of periodicity, kurtosis, skewness and self-similarity of a time series and investigate the performance of these parameters in separating DDoS attack from normal traffic.

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Section: Periodicitymentioning

confidence: 99%

Section: Periodicitymentioning

confidence: 99%

See 1 more Smart Citation

Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

Kayatas

Fouladi

Ermis

et al. 2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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show abstract

“…A well known estimator of the PSD is the periodogram, which is a vector comprised of the squared magnitude of the Fourier coefficients. We use AUTOPERIOD [26], a two-tier approach by considering the information in both the autocorrelation and the periodogram, to detect periods for each word. Unfortunately, the method fails to detect meaningful periodic words because the time series are sparse and few words have apparent periodic patterns.…”

Section: B Qualitative Evaluation 1) Topics Discovered By Lptamentioning

confidence: 99%

“…Due to the importance of periodicity analysis, many research works have been proposed in periodicity detection for time series database [20], [9], [30], [7], [26]. Some studies follow the similar strategies to analyze the time distribution of a single tag or query to detect periodic patterns [25], [6], [22].…”

Section: Introductionmentioning

confidence: 99%

LPTA: A Probabilistic Model for Latent Periodic Topic Analysis

Yin

Cao

Zhai

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-This paper studies the problem of latent periodic topic analysis from timestamped documents. The examples of timestamped documents include news articles, sales records, financial reports, TV programs, and more recently, posts from social media websites such as Flickr, Twitter, and Facebook. Different from detecting periodic patterns in traditional time series database, we discover the topics of coherent semantics and periodic characteristics where a topic is represented by a distribution of words. We propose a model called LPTA (Latent Periodic Topic Analysis) that exploits the periodicity of the terms as well as term co-occurrences. To show the effectiveness of our model, we collect several representative datasets including Seminar, DBLP and Flickr. The results show that our model can discover the latent periodic topics effectively and leverage the information from both text and time well.

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Periodic Cuscore charts to detect step shifts in autocorrelated processes

Changpetch¹,

Nembhard²

2008

Quality & Reliability Eng

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Among statistical control charts, the Cuscore control chart has been designed specifically for particular types of signals and process models. The synchronization between the residuals of the process and the detectors of the Cuscore makes this control chart more sensitive for detecting a signal than the Shewhart, cusum and other standard control charts. However, this synchronization happens only when the time of the occurrence of the signal is known. In this paper, we develop two approaches for applying the Cuscore chart when the time of the signal is unknown. The first approach is to reinitialize the Cuscore statistic with a prescribed cycle. The second approach considers only the most recent time periods to calculate the Cuscore statistic. We apply these two approaches to construct what we call periodic Cuscore control charts to detect step shifts in seasonal time series processes. Simulation results show that the performance of periodic Cuscore charts, in terms of average run length to signal a special cause, is better than that of cusum charts in most cases even when faced with a long mismatch period between the time of resetting the charts and the time of the occurrence of the signal. These results indicate that the two approaches are practical and effective when the time of the signal is unknown.

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On Periodicity Detection and Structural Periodic Similarity

Cited by 202 publications

References 15 publications

Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

LPTA: A Probabilistic Model for Latent Periodic Topic Analysis

Periodic Cuscore charts to detect step shifts in autocorrelated processes

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