[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.
李勇男. 贝叶斯理论在反恐情报分类分析中的应用研究*[J]. 数据分析与知识发现, 2018, 2(10): 9-14.
Li Yongnan. Using Bayes Theory to Classify Counter Terrorism Intelligence. Data Analysis and Knowledge Discovery, 2018, 2(10): 9-14.
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.
(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.)
doi: 10.16353/j.cnki.1000-7490.2017.09.025
[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.
doi: 10.1080/02684527.2014.988445
[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.
doi: 10.1016/j.eswa.2016.09.029
[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.
doi: 10.1109/TIFS.2016.2622226
[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.
(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.)
doi: 10.3321/j.issn:1000-0054.2002.06.005
[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.
doi: 10.2307/1401634
[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.
doi: 10.1007/s11704-014-3038-5
[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.)
(Cao Jianfang, Wang Hongbin.A New Algorithm of SVM-based Incremental Learning for Text Classification[J]. E-Business, 2009(12): 68-71.)
doi: 10.3969/j.issn.1009-6108.2009.12.032
(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.)
(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].
