| 利用会话模式的垃圾电话检测 |
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| 引用本文： | 胡楠,袁庆升,魏更宇,黄玮,杨义先.利用会话模式的垃圾电话检测[J].北京邮电大学学报,2011,34(1):130-134. |
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| 作者姓名： | 胡楠 袁庆升 魏更宇 黄玮 杨义先 |
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| 作者单位： | 北京邮电大学网络与信息攻防技术教育部重点实验室,北京,100876;北京邮电大学,灾备技术国家工程实验室,北京,100876;国家计算机网络应急技术处理协调中心,北京,100029 |
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| 基金项目： | 国家242信息安全计划项目(2009A83)； 国家高技术研究发展计划项目(2008AA011004)； 国家重大科技专项项目(2009ZX0300400303) |
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| 摘 要： | 为了解决网络电话(VoIP)业务面临的Internet垃圾电话(SPIT)问题,在针对SPIT检测方案的基础上提出利用人类会话模型对会话进行建模,在该模型的基础上将会话转化为表征会话模式的状态序列,再利用朴素贝叶斯分类器计算得到该会话模式为恶意会话模式可能性的数值度量.该数值度量可用作检测呼叫机器人制造垃圾电话的依据,并在VoIP边界防护系统中得到了应用.实验结果表明,该方法在较低误报率(2%左右)的情况下能保证较低的漏报率(小于10%),同时证明了会话模式可作为判断SPIT的特性之一.
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| 关 键 词： | Internet垃圾电话 端点检测 评分系统 漏报率 误报率 |
| 收稿时间： | 2010-03-24 |
| 修稿时间： | 2010-08-17 |
A SPIT Detection Method Based on Conversation Pattern |
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| HU Nan,YUAN Qing-sheng,WEI Geng-yu,HUANG Wei,YANG Yi-xian.A SPIT Detection Method Based on Conversation Pattern[J].Journal of Beijing University of Posts and Telecommunications,2011,34(1):130-134. |
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| Authors: | HU Nan YUAN Qing-sheng WEI Geng-yu HUANG Wei YANG Yi-xian |
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| Affiliation: | HU Nan1,2,YUAN Qing-sheng3,WEI Geng-yu1,HUANG Wei1,YANG Yi-xian1,2(1.Key Laboratory of Network and Information Attack and Defense Technology,Ministry of Education,Beijing University of Posts and Telecommunications,Beijing 100876,China,2.National Engineering Laboratory for Disaster Backup and Recovery,3.National Computer Network Emergency Response Technical Team/Coordination Center of China,Beijing 100029,China) |
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| Abstract: | Based on the existing solution for spam over Internet telephony(SPIT) detection,a method using human conversational model to describe the call pattern of human telephone call is proposed.A conversational pattern state sequence is generated through the conversational model from a call.Via using Bayesian probability model,the possibility that the call is malicious is calculated.The possibility can be used to detect the spam phone calls generated by robot,and can be applied in our perimeter defense system.Expe... |
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| Keywords: | spam over Internet telephony endpoint detection scoring system false negative rate false positive rate |
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