摘要
为分析恐怖组织不同维度的特征数据及特征之间的内在联系,在全球恐怖主义数据库中选取5个典型国际恐怖组织,基于特征画像,运用统计学、机器学习、关联规则挖掘以及地理信息系统数据分析方法,对恐怖袭击特征属性进行分析。结果表明,5个恐怖组织偏好不同的袭击区域,但普遍偏好炸弹/炸药与枪支类武器;在特征关联上,攻击类型、目标类型和武器类型3类属性的特征关联较为明显。该方法在反恐情报分析中适用于挖掘不同涉恐人员的特征差异。
In order to effectively analyze multidimensional characteristic data of terrorist organizations and the interrelationship between the characteristic data,we select five typical international terrorist organizations from the Global Terrorism Database.Based on feature profiling,we use the method of statistics,machine learning,association rule mining and geographic information system to analyze the attacking characteristic attributes.According to the results,attacking preferences of the five typical terrorist organizations are considerably related to the attack regions but they all prefer to choose the weapons of explosives/bombs and firearms;in the association characteristic attributes,the associations of the three types of attributes,namely attack type,target type and weapon type are obviously correlated.The method is suitable for mining the characteristics of different potential terrorists in the intelligence analysis of counter-terrorism.
作者
唐正
朱衍丞
邱凌峰
郑超慧
TANG Zheng;ZHU Yan-cheng;QIU Ling-feng;ZHENG Chao-hui(School of Information Technology and Cyber Security,People's Public Security University of China;Key Laboratory of Security Technology&Risk Assessment,Ministry of public security,Beijing 102628,China)
出处
《软件导刊》
2019年第1期128-131,143,共5页
Software Guide
基金
国家自然科学基金项目(71704183)
关键词
恐怖组织
特征画像
全球恐怖主义数据库
关联规则挖掘
梯度提升决策树
terrorist organization
feature profiling
the global terrorism database
association rule mining
gradient boosting decision tree
