Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/25101
Title: | Evolutionary ensemble generative adversarial learning for identifying terrorists among high-speed rail passengers |
Authors: | Zheng, Y-J Gao, C-C Huang, Y-J Sheng, W-G Wang, Z |
Keywords: | anti-terrorism;classification;deep learning;ensemble learning;evolutionary algorithm;generative adversarial network (GAN) |
Issue Date: | 8-Aug-2022 |
Publisher: | Elsevier |
Citation: | Zheng, Y.-J. et al. (2022 ) 'Evolutionary ensemble generative adversarial learning for identifying terrorists among high-speed rail passengers', Expert Systems with Applications, 118430, pp. 1 - 12. doi: 10.1016/j.eswa.2022.118430. |
Abstract: | As one of the most salient features of China’s economic development, high-speed rail (HSR) is considered to be an attractive target and travel mode for terrorists. Distinguishing potential terrorists from normal passengers is of critical importance to public security, but very challenging because terrorists constitute only a very small fraction of HSR passengers, especially when they can disguise their attributes and behaviors to deceive the classifiers. For this extremely imbalanced classification problem, we propose a novel evolutionary generative adversarial network (GAN) ensemble method, where each GAN in the ensemble simultaneously trains a discriminator to identify abnormal samples from a large number of passenger profiles and trains a generator to produce abnormal samples that are disguised as normal ones in a subspace of the sample space, and the final classifier combines these GANs using an evolutionary fusion method. Experiments on benchmark problems demonstrate that the proposed method has very competitive performance compared to popular imbalanced classifiers. The successful applications in terrorist identification for China Railway also demonstrate the feasibility and effectiveness of our approach. |
Description: | Data availability: I have shared the link to my data/code at our website (http://compintell.cn/en/dataAndCode.html). |
URI: | https://bura.brunel.ac.uk/handle/2438/25101 |
DOI: | https://doi.org/10.1016/j.eswa.2022.118430 |
ISSN: | 0957-4174 |
Other Identifiers: | 118430 |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2022 Elsevier. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ (see: https://www.elsevier.com/about/policies/sharing). | 784.91 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License