Journal of Terahertz Science and Electronic Information Technology , Volume. 20, Issue 12, 1257(2022)

Benchmark datasets for insider threat detection and indoor crowd behavior analysis

ZHAO Ying1、*, ZHAO Xin1, YANG Kui1, CHEN Siming2, ZHANG Zhuo3, and HUANG Xin3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    Benchmark datasets are crucial for many data-dependent scientific studies and technology applications. Academic and industry communities have closely collaborated to release abundant datasets in many fields. However, there is still a lack of high-quality benchmark datasets in some specific domains. This paper introduces two open-source benchmark datasets, namely, the Insider Threat Dataset(ITD-2018) and the Indoor Crowd Movement Trajectory Dataset(ICMTD-2019). The two datasets are produced by program-driven synthetic data generation methods and are presented with well-defined scenarios, carefully-designed behavior models, rich data patterns, and vivid storylines. The two datasets were used in the ChinaVis Data Challenge. This paper aims to promote the two datasets for the development of the research and technology in relevant domains.

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    ZHAO Ying, ZHAO Xin, YANG Kui, CHEN Siming, ZHANG Zhuo, HUANG Xin. Benchmark datasets for insider threat detection and indoor crowd behavior analysis[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(12): 1257

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    Paper Information

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    Received: Apr. 8, 2021

    Accepted: --

    Published Online: Feb. 17, 2023

    The Author Email: Ying ZHAO (zhaoying@csu.edu.cn)

    DOI:10.11805/tkyda2021143

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