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Sequential probability ratio test

About: Sequential probability ratio test is a research topic. Over the lifetime, 1248 publications have been published within this topic receiving 22355 citations.


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Journal ArticleDOI
TL;DR: The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly, yielding a bivariate model that eliminates most false positives.
Abstract: This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phase or manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit-rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less.

155 citations

Proceedings ArticleDOI
17 Oct 2005
TL;DR: The R-RANSAC with SPRT which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy is introduced and is the fastest possible (on average) of all randomized RANSAC algorithms guaranteeing 1 - n confidence in the solution.
Abstract: A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability n. A provably optimal model verification strategy is designed for the situation when the contamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized RANSAC algorithms guaranteeing 1 - n confidence in the solution. The derivation of the optimality property is based on Wald's theory of sequential decision making. The R-RANSAC with SPRT which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy is introduced. We show experimentally that on standard test data the method is 2 to 10 times faster than the standard RANSAC and up to 4 times faster than previously published methods

154 citations

Journal ArticleDOI
18 Feb 2015-Neuron
TL;DR: This work trained rhesus monkeys to make decisions based on a sequence of evanescent, visual cues assigned different logLR, hence different reliability, and found that monkeys' choices and reaction times were explained by LIP activity in the context of accumulation of logLR to a threshold.

143 citations

Book ChapterDOI
01 Jan 1983
TL;DR: This chapter presents a decision procedure that operates sequentially and can easily be applied to tailored testing without loss of any of the elegance and mathematical sophistication of the examination procedures.
Abstract: Publisher Summary This chapter presents a decision procedure that operates sequentially and can easily be applied to tailored testing without loss of any of the elegance and mathematical sophistication of the examination procedures. In applying the decision procedure, two specific item response theory (IRT) models are used: the one- and three-parameter logistic models. Although any other IRT model could just as easily have been used, these models were selected because of their frequent appearance in the research literature and because of the existence of readily available calibration programs and tailored testing programs. The purposes of this research were (1) to obtain information on how the sequential probability ratio test (SPRT) procedure functioned when items were not randomly sampled from the item pool; (2) to gain experience in selecting the bounds of the indifference region; and (3) to obtain information on the effects of guessing on the accuracy of classification when the one-parameter logistic model was used. To determine the effects of these variables, the computation of the SPRT was programmed into both the one- and three-parameter logistic tailored testing procedures that were operational at the University of Missouri—Columbia.

134 citations

Book
01 Sep 1989
TL;DR: In this article, auxiliary signals are used for improving fault detection in the chemical process, and a sequential probability ratio test is used to detect faults in a chemical process with auxiliary signals.
Abstract: Preliminaries.- Sequential probability ratio test.- Auxiliary signals for improving fault detection.- Extension to multiple hypothesis testing.- Modelling and identification of the chemical process.- Fault detection and diagnosis in the chemical process.- Conclusions and further research.

132 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
202223
202129
202023
201929
201832