Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 9-14    DOI: 10.11925/infotech.2096-3467.2018.0708
Current Issue | Archive | Adv Search |
Using Bayes Theory to Classify Counter Terrorism Intelligence
Yongnan Li()
School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100038, China
Download: PDF(458 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study modifies Naive Bayes Classifier according to the features of counterterrorism intelligence, aiming to provide a simple and practical way to categorize these data. [Methods] Firstly, we deleted the outliers of terrorism related data, discretized continuous attributes, as well as finished reduction of data with high level correlation. Secondly, we computed conditional probabilities of different attributes. Lastly, we classified new sample dataset based on maximum posteriori hypothesis. [Results] After categorizing the data, we raised probability threshold to partially offset the influence of the data dependence. Only some data of high-level sensitivity needs to be process manually. [Limitations] This method has some restrictions on data independence. In practice, it must be combined with other classification method such as decision tree to cover more intelligence data, and provide information for early warning. [Conclusions] The proposed method, which increases the efficiency of intelligence analysis, is ease of use and has fewer restrictions on the intelligence analysts.

Key wordsBayes Theory      Naive Bayes      Maximum Posteriori Hypothesis      Counter Terrorism Intelligence      Data Mining     
Received: 03 July 2018      Published: 12 November 2018

Cite this article:

Yongnan Li. Using Bayes Theory to Classify Counter Terrorism Intelligence. Data Analysis and Knowledge Discovery, 2018, 2(10): 9-14.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0708     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I10/9

[1] Thuraisingham B.Data Mining for Counter-Terrorism[A]// Kargupta H, Joshi A, Sivakumar K, et al. Data Mining: Next Generation Challenges and Future Directions[M]. 2004: 157-183.
[2] 郭璇, 吴文辉, 肖治庭, 等. 基于深度学习和公开来源信息的反恐情报挖掘[J]. 情报理论与实践, 2017, 40(9):135-139.
[2] (Guo Xuan, Wu Wenhui, Xiao Zhiting, et al.Anti-terrorism Intelligence Mining Based on Deep Learning and Open Source Information[J]. Information Studies: Theory & Application, 2017, 40(9): 135-139.)
[3] Ahmed A A, Zaman N A K. Attack Intention Recognition: A Review[J]. International Journal of Network Security, 2017, 19(2): 244-250.
[4] Argomaniz J, Bures O, Kaunert C.A Decade of EU Counter-terrorism and Intelligence: A Critical Assessment[J]. Intelligence and National Security, 2015, 30(2-3): 191-206.
[5] Chenoweth E, Lowham E.On Classifying Terrorism: A Potential Contribution of Cluster Analysis for Academics and Policy-makers[J]. Defence & Security Analysis, 2007, 23(4): 345-357.
[6] Colladon A F, Remondi E.Using Social Network Analysis to Prevent Money Laundering[J]. Expert Systems with Applications, 2017, 67: 49-58.
[7] Gunturi V M V, Shekhar S. Big Spatio-temporal Network Data Analytics for Smart Cities: Research Needs[A]// Thakuriah P, Tilahun N, Zellner M. Seeing Cities Through Big Data [M]. Springer International Publishing, 2017: 127-140.
[8] Chen N, Chen Y, Ye X, et al.Smart City Surveillance in Fog Computing[A]// Mavromoustakis C, Mastorakis G, Dobre C, et al. Advances in Mobile Cloud Computing and Big Data in the 5G Era[M]. Springer International Publishing, 2017: 203-226.
[9] Jayasree V, Balan R V S. Anti Money Laundering in Financial Institutions Using Affiliation Mapping Calculation and Sequential Mining[J]. Journal of Engineering and Applied Sciences, 2016, 11(1): 51-56.
[10] Taha K, Yoo P D.Using the Spanning Tree of a Criminal Network for Identifying Its Leaders[J]. IEEE Transactions on Information Forensics and Security, 2017, 12(2): 445-453.
[11] Knoke D.Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism[A]// Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource[M]. John Wiley & Sons, Inc., 2015:1-15.
[12] 刘红岩, 陈剑, 陈国青. 数据挖掘中的数据分类算法综述[J]. 清华大学学报: 自然科学版, 2002, 42(6): 727-730.
[12] (Liu Hongyan, Chen Jian, Chen Guoqing.Review of Classification Algorithms for Data Mining[J]. Journal of Tsinghua University: Science and Technology, 2002, 42(6): 727-730.)
[13] 钱晓东. 数据挖掘中分类方法综述[J]. 图书情报工作, 2007, 51(3):68-71.
[13] (Qian Xiaodong.A Review on Classification Algorithms in Data Mining[J]. Library & Information Service, 2007, 51(3): 68-71.)
[14] Cornfield J.Bayes Theorem[J]. Revue De Linstitut International De Statistique, 1967, 35(1): 34-49.
[15] Lindley D V.Fiducial Distributions and Bayes’ Theorem[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1958, 20(1): 102-107.
[16] Li C, Jiang L, Li H.Naive Bayes for Value Difference Metric[J]. Frontiers of Computer Science, 2014, 8(2): 255-264.
[17] Tan P N, Steinbach M, Kumar V.数据挖掘导论[M]. 范明, 范宏建译. 第2版. 北京: 人民邮电出版社, 2011.
[17] (Tan P N, Steinbach M, Kumar V.Introduction to Data Mining[M]. Translated by Fan Ming, Fan Hongjian. The 2nd Edition. Beijing: The People’s Posts and Telecommunications Press, 2011.)
[18] 曹建芳, 王鸿斌. 一种新的基于SVM的文本分类增量学习算法[J]. 电子商务, 2009(12): 68-71.
[18] (Cao Jianfang, Wang Hongbin.A New Algorithm of SVM-based Incremental Learning for Text Classification[J]. E-Business, 2009(12): 68-71.)
[19] 李勇男, 梅建明, 秦广军. 反恐情报分析中的数据预处理研究[J]. 情报科学, 2017, 35(11): 103-107,113.
[19] (Li Yongnan, Mei Jianming, Qin Guangjun.Research on Data Preprocessing in the Field of Counter Terrorism Intelligence Analysis[J]. Information Science, 2017, 35(11): 103-107,113.)
[20] 古丽阿扎提·吐尔逊. “东突”恐怖势力个体特征及其发展趋势评析[J]. 现代国际关系, 2014(1):56-62.
[20] (Tursun Gulazat.On Individual Features of “Eastern Turkistan” Terrorism and Its Future Developments[J]. Contemporary International Relations, 2014(1): 56-62.)
[21] 石河子大学. 宗教极端活动的75种具体表现[EB/OL]. [2018-06-09]. .
[21] (Shihezi University. 75 Types of Manifestation of Religious Extremism [EB/OL]. [2018-06-09].
[22] 网易新闻. 穿戴蒙面罩袍是极端化的行为表现[EB/OL]. [2018-06-09]. .
[22] (NetEase News. It is Extreme Behavior to Wear a Face-covering Burqa[EB/OL]. [2018-06-09].
[23] 李勇男. 基于雅卡尔系数的反恐情报聚类分析[J]. 现代情报, 2018, 38(1): 51-55.
[23] (Li Yongnan.Clustering Analysis of Counter Terrorism Intelligence Based on Jaccard Index[J]. Journal of Modern Information, 2018, 38(1): 51-55.)
[1] Yong Zhang,Shuqing Li,Yongshang Cheng. Mining Algorithm for Weighted Association Rules Based on Frequency Effective Length[J]. 数据分析与知识发现, 2019, 3(7): 85-93.
[2] Quan Lu,Anqi Zhu,Jiyue Zhang,Jing Chen. Research on User Information Requirement in Chinese Network Health Community: Taking Tumor-forum Data of Qiuyi as an Example[J]. 数据分析与知识发现, 2019, 3(4): 22-32.
[3] Dongmei Mu,Hui Fa,Ping Wang,Jing Sun. Research on Disease Risk Factors on Structural Equation Model[J]. 数据分析与知识发现, 2019, 3(4): 80-89.
[4] Dongmei Mu,Ping Wang,Danning Zhao. Reducing Data Dimension of Electronic Medical Records: An Empirical Study[J]. 数据分析与知识发现, 2018, 2(1): 88-98.
[5] Zhongyi Hu,Chaoqun Wang,Jiang Wu. Identifying Phishing Websites with Multiple Online Data Sources[J]. 数据分析与知识发现, 2017, 1(6): 47-55.
[6] Siwei Jiang,Zhenping Xie,Meijie Chen,Ming Cai. Self-Explainable Reduction Method for Mixed Feature Data Modeling[J]. 数据分析与知识发现, 2017, 1(12): 92-100.
[7] Mu Dongmei,Ren Ke. Discovering Knowledge from Electronic Medical Records with Three Data Mining Algorithms[J]. 现代图书情报技术, 2016, 32(6): 102-109.
[8] Li Feng,Li Shu’ning,Yu Jing. A Department Oriented Library Usage Data System for Graduates[J]. 现代图书情报技术, 2016, 32(5): 99-103.
[9] Zhao Jingxian. Detect of Internet Fake Public Opinion Based on Decision Tree[J]. 现代图书情报技术, 2015, 31(6): 78-84.
[10] He Jianmin, Wang Zhe. The Pedigree Method to Mine Influential Clusters of Topic Information in Social Network[J]. 现代图书情报技术, 2015, 31(5): 65-72.
[11] Huang Wenbin, Xu Shanchuan, Ma Long, Wang Jun. Analysis of Mobile User Behaviors with Telecommunication Data[J]. 现代图书情报技术, 2015, 31(5): 80-87.
[12] Tang Xiangbin, Lu Wei, Zhang Xiaojuan, Huang Shihao. Feature Analysis and Automatic Identification of Query Specificity[J]. 现代图书情报技术, 2015, 31(2): 15-23.
[13] Ma Bin, Yin Lifeng. A Parallel Naive Bayesian Network Public Opinion Fast Classification Algorithm Based on Hadoop Platform[J]. 现代图书情报技术, 2015, 31(2): 78-84.
[14] Hao Mei, Wang Daoping. Mining Customer Focus Features from Product Reviews Oriented Supply Chain[J]. 现代图书情报技术, 2014, 30(4): 65-70.
[15] Sun Hongfei, Hou Wei. Application of Improved TFIDF Algorithm in Mining Potential Cooperation Relationship[J]. 现代图书情报技术, 2014, 30(10): 84-92.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn