Accelerating transfer entropy computation

Shao, Shengjia; Guo, Ce; Luk, Wayne; Weston, Stephen

doi:10.1109/fpt.2014.7082754

Cited by 16 publications

(21 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By converting the conditional entropy tree structures into arrays, batches of packet data can be converted into a format that is easily parallelized and translated into GPU code. This technique could potentially be applied to other entropy measurements, such as those used to evaluate financial transfers or connectivity in the brain [15]. Our results demonstrate that, in addition to tasks such as firewall rule lookups and packet forwarding [2], GPU packet processing can be applied for improving statistical analysis of individual network flows at the packet level.…”

Section: Discussionmentioning

confidence: 98%

“…For example, it can be used to measure a person's influence on social media such as Twitter, or to measure the connectivity in brain regions [15]. However, for a large value series, entropy calculations can become very timeconsuming [15]. Therefore, improving the efficiency of entropy measurements using GPU processing is a worthwhile pursuit beyond its applications for covert channel detection.…”

Section: B Motivationmentioning

confidence: 99%

“…Transfer entropy has applications in data mining and neuroscience, among other uses. For example, it can be used to measure a person's influence on social media such as Twitter, or to measure the connectivity in brain regions [15]. However, for a large value series, entropy calculations can become very timeconsuming [15].…”

Section: B Motivationmentioning

confidence: 99%

“…Corrected conditional entropy in particular is useful for evaluating heart rate data and other bioinformatics [6]. Conditional entropy alone is useful for a variety of applications, including analyzing financial time series data [15]. Transfer entropy has applications in data mining and neuroscience, among other uses.…”

Section: B Motivationmentioning

confidence: 99%

See 3 more Smart Citations

Real-time GPU-based timing channel detection using entropy

Gegan

Ahuja

Owens

et al. 2016

2016 IEEE Conference on Communications and Network Security (CNS)

View full text Add to dashboard Cite

Abstract-As line rates continue to grow, network security applications such as covert timing channel (CTC) detection must utilize new techniques for processing network flows in order to protect critical enterprise networks. GPU-based packet processing provides one means of scaling the detection of CTCs and other anomalies in network flows. In this paper, we implement a GPUbased detection tool, capable of detecting model-based covert timing channels (MBCTCs). The GPU's ability to process a large number of packets in parallel enables more complex detection tests, such as the corrected conditional entropy (CCE) test-a modified version of the conditional entropy measurement, which has a variety of applications outside of covert channel detection. In our experiments, we evaluate the CCE test's true and false positive detection rates, as well as the time required to perform the test on the GPU. Our results demonstrate that GPU packet processing can be applied successfully to perform real-time CTC detection at near 10 Gbps with high accuracy.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: B Motivationmentioning

confidence: 99%

Section: B Motivationmentioning

confidence: 99%

Section: B Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Real-time GPU-based timing channel detection using entropy

Gegan

Ahuja

Owens

et al. 2016

2016 IEEE Conference on Communications and Network Security (CNS)

View full text Add to dashboard Cite

show abstract

“…Hence, the time complexity of VL-G is O(T δ max ). e time complexity of TE can be at most O(T 3 ) [37], which makes VL-TE has the same time complexity. However, with the work Table 2 shows the running time of our approach on time series with the varying length (T ∈ {5000, 20000}) and maximum time delay (δ max ∈ {0.05T , 0.1T , 0, 2T }).…”

Section: Time Complexity and Running Timementioning

confidence: 99%

Variable-Lag Granger Causality for Time Series Analysis

Amornbunchornvej

Zheleva

Berger-Wolf

2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

View full text Add to dashboard Cite

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the e ect time series is in uenced by a combination of other time series with a xed time delay. e assumption of xed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the xed time delay does not hold in many applications, such as collective behavior, nancial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the xed time delay and allow causes to in uence e ects with arbitrary time delays. In addition, we propose a method for inferring both variable-lag Granger causality and Transfer Entropy relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform be er than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. e so ware of this work is available in the R-CRAN package: VLTimeCausality.

show abstract