利用突发探测技术,识别过境处的可疑车辆

S. Kaza, Hsin-Min Lu, D. Zeng, Hsinchun Chen
{"title":"利用突发探测技术,识别过境处的可疑车辆","authors":"S. Kaza, Hsin-Min Lu, D. Zeng, Hsinchun Chen","doi":"10.1109/ISI.2012.6284311","DOIUrl":null,"url":null,"abstract":"Border safety is a critical part of national and international security. The Department of Homeland Security (DHS) searches vehicles entering the country at land borders for drugs and other contraband. However, this process is time-consuming and operational efficiency is needed for smooth operations at the border. To aid in the screening of vehicles, we propose to examine traffic patterns at checkpoints using burst detection algorithms. Our results show that the overall traffic at the border shows bursting patterns attributable to week days and the holiday seasons. In addition, using local law-enforcement data we also find that traffic with prior contacts with law-enforcement shows a bursting pattern distinct from other traffic. We also find that such bursts in suspicious traffic can be attributable to increases in vehicular traffic associated with certain kinds of criminal activity. This information can be used to specifically target vehicles searches during primary screening at ports and in the surrounding areas.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Using burst detection techniques to identify suspicious vehicular traffic at border crossings\",\"authors\":\"S. Kaza, Hsin-Min Lu, D. Zeng, Hsinchun Chen\",\"doi\":\"10.1109/ISI.2012.6284311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Border safety is a critical part of national and international security. The Department of Homeland Security (DHS) searches vehicles entering the country at land borders for drugs and other contraband. However, this process is time-consuming and operational efficiency is needed for smooth operations at the border. To aid in the screening of vehicles, we propose to examine traffic patterns at checkpoints using burst detection algorithms. Our results show that the overall traffic at the border shows bursting patterns attributable to week days and the holiday seasons. In addition, using local law-enforcement data we also find that traffic with prior contacts with law-enforcement shows a bursting pattern distinct from other traffic. We also find that such bursts in suspicious traffic can be attributable to increases in vehicular traffic associated with certain kinds of criminal activity. This information can be used to specifically target vehicles searches during primary screening at ports and in the surrounding areas.\",\"PeriodicalId\":199734,\"journal\":{\"name\":\"2012 IEEE International Conference on Intelligence and Security Informatics\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Intelligence and Security Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISI.2012.6284311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Intelligence and Security Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISI.2012.6284311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

边境安全是国家和国际安全的重要组成部分。国土安全部(DHS)在陆地边境对进入该国的车辆进行毒品和其他违禁品搜查。然而,这一过程耗时长,而且需要提高操作效率,才能在边境顺利进行。为了帮助筛选车辆,我们建议使用突发检测算法检查检查站的交通模式。我们的研究结果表明,边境的总体交通呈现出可归因于工作日和假日季节的爆发模式。此外,利用当地执法数据,我们还发现,与执法部门有过事先接触的交通表现出与其他交通不同的爆发模式。我们还发现,这种可疑交通的爆发可以归因于与某些犯罪活动相关的车辆交通的增加。这一信息可用于在港口和周边地区进行初步筛查期间专门针对车辆进行搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Using burst detection techniques to identify suspicious vehicular traffic at border crossings
Border safety is a critical part of national and international security. The Department of Homeland Security (DHS) searches vehicles entering the country at land borders for drugs and other contraband. However, this process is time-consuming and operational efficiency is needed for smooth operations at the border. To aid in the screening of vehicles, we propose to examine traffic patterns at checkpoints using burst detection algorithms. Our results show that the overall traffic at the border shows bursting patterns attributable to week days and the holiday seasons. In addition, using local law-enforcement data we also find that traffic with prior contacts with law-enforcement shows a bursting pattern distinct from other traffic. We also find that such bursts in suspicious traffic can be attributable to increases in vehicular traffic associated with certain kinds of criminal activity. This information can be used to specifically target vehicles searches during primary screening at ports and in the surrounding areas.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Detecting criminal networks: SNA models are compared to proprietary models Securing cyberspace: Identifying key actors in hacker communities Emergency decision support using an agent-based modeling approach Payment card fraud: Challenges and solutions Extracting action knowledge in security informatics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1