Optimal cost allocation in centralized and decentralized detection systems using Bhattacharyya distance

Laz, Eray

doi:10.1109/radar.2017.7944381

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…In [112], Baskoro et al proposed an algorithm for counting and tracking vehicles using the Bhattacharyya Distance. It is used by Laz in [113] to evaluate detection system performance. In Table 9, there is a summary of works around Bhattacharyya Distance.…”

Section: ) Bhattacharyya Distancementioning

confidence: 99%

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A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

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Internet traffic classification aims to identify the kind of Internet traffic. With the rise of traffic encryption and multi-layer data encapsulation, some classic classification methods have lost their strength. In an attempt to increase classification performance, Machine Learning (ML) strategies have gained the scientific community interest and have shown themselves promising in the future of traffic classification, mainly in the recognition of encrypted traffic. However, some of these methods have a high computational resource consumption, which make them unfeasible for classification of large traffic flows or in real-time. Methods using statistical analysis have been used to classify real-time traffic or large traffic flows, where the main objective is to find statistical differences among flows or find a pattern in traffic characteristics through statistical properties that allow traffic classification. The purpose of this work is to address statistical methods to classify Internet traffic that were little or unexplored in the literature. This work is not generally focused on discussing statistical methodology. It focuses on discussing statistical tools applied to Internet traffic classification Thus, we provide an overview on statistical distances and divergences previously used or with potential to be used in the classification of Internet traffic. Then, we review previous works about Internet traffic classification using statistical methods, namely Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback-Leibler (KL) divergences, Support Vector Machines (SVM), Correlation Information (Pearson Correlation), Kolmogorov-Smirnov and Chi-Square tests, and Entropy. We also discuss some open issues and future research directions on Internet traffic classification using statistical methods.

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Section: ) Bhattacharyya Distancementioning

confidence: 99%

“…N/A Baskoro et al [112] 2017 Features: Probability Density Function (PDF), number of pixel, and color pdf's. P:96.5, R:96.3 Laz [113] 2017 Techniques: lagrange multipliers technique, and parallel computing. N/A Shah and Dang [114]…”

Section: N/amentioning

confidence: 99%