Big data analytics for security and criminal investigations

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 7, Issue 4, 2017, Pages

  Pramanik, M I ^a   Lau, Raymond Y K ^a   Yue, Wei T ^a   Ye, Yunming ^b   Li, Chunping ^c  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CRIME; DATA MINING; INTELLIGENT AGENTS; LAW ENFORCEMENT; LEARNING SYSTEMS;

COMMON DATUM; CRIMINAL INVESTIGATION; CUTTING EDGES; DATA ANALYTICS; FUTURE RESEARCH DIRECTIONS; POSITIONAL ANALYSIS; STATE-OF-THE-ART TECHNOLOGY; TEXT MINING;

BIG DATA;

EID: 85019062819     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1208     Document Type: Review

References (110)

1. McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D. Big data. The management revolution. Harvard Bus Rev 2012, 90:61–67.
2. Xu J, Chen H. Criminal network analysis and visualization. Commun ACM 2005, 48:100–107.
3. Chibelushi C, Sharp B, Shah H. ASKARI: a crime text mining approach. Digital Crime Forensic Sci Cyberspace 2006, 30:155.
4. Kaisler S, Armour F, Espinosa JA, Money W. Big data: Issues and challenges moving forward. In: System sciences (HICSS), 2013 46th Hawaii International Conference, IEEE, 17 January, 2013, 995–1004.
5. Van der Hulst RC. Introduction to social network analysis (SNA) as an investigative tool. Trends Organ Crime 2009, 12:101–121.
6. Tan W, Blake MB, Saleh I, Dustdar S. Social-network-sourced big data analytics. IEEE Internet Comput 2013, 17:62–69.
7. Han J, Pei J, Kamber M. Data Mining: Concepts and Techniques. Amsterdam, Netherlands: Elsevier; 2011.
8. Tufféry S. Data mining and statistics for decision making.
9. Oatley G, Ewart B, Zeleznikow J. Decision support systems for police: lessons from the application of data mining techniques to “soft” forensic evidence. Artif Intell Law 2006, 14:35–100.
10. Getoor L. Link mining: a new data mining challenge. ACM SIGKDD Explor Newslett 2003, 5:84–89.
11. Schroeder J, Xu J, Chen H, Chau M. Automated criminal link analysis based on domain knowledge. J Am Soc Inf Sci Technol 2007, 58:842–855.
12. Gilbert E, Karahalios K. Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on Human Factors in Computing Systems, ACM, 4 April, 2009, 211–220.
13. Klerks P. The network paradigm applied to criminal organizations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands. Connections 2001, 24:53–65.
14. Schroeder J, Xu J, Chen H. Crimelink explorer: Using domain knowledge to facilitate automated crime association analysis. In: International Conference on Intelligence and Security Informatics, 2 June, 2003, 168–180. Berlin, Heidelberg: Springer.
15. Yang CC, Shi X, Wei CP. Tracing the event evolution of terror attacks from on-line news. In: International Conference on Intelligence and Security Informatics, 23 May, 2006, 343–354. Berlin, Heidelberg: Springer.
16. Kim Y, Choi TY, Yan T, Dooley K. Structural investigation of supply networks: a social network analysis approach. J Oper Manage 2011, 29:194–211.
17. Zhao Z, Feng S, Wang Q, Huang JZ, Williams GJ, Fan J. Topic oriented community detection through social objects and link analysis in social networks. Knowl Syst 2012, 26:164–173.
18. Brenner W, Zarnekow R, Wittig H. Intelligent Software Agents: Foundations and Applications. Berlin, Germany: Springer Science & Business Media; 2012.
19. Jain M, Tsai J, Pita J, Kiekintveld C, Rathi S, Tambe M, Ordónez F. Software assistants for randomized patrol planning for the lax airport police and the federal air marshal service. Interfaces 2010, 40:267–290.
20. Bard JF. Practical Bilevel Optimization: Algorithms and Applications. Berlin, Germany: Springer Science & Business Media; 2013:9.
21. Fricker RD Jr, Rolka H. Protecting against biological terrorism: statistical issues in electronic biosurveillance. Chance 2006, 19:4–14.
22. Jaber A, Guarnieri F, Wybo JL. Intelligent software agents for forest fire prevention and fighting. Safety Sci 2001, 39:3–17.
23. Taylor M, Haggerty J, Gresty D, Almond P, Berry T. Forensic investigation of social networking applications. Netw Security 2014, 2014:9–16.
24. Mena J. Investigative Data Mining for Security and Criminal Detection. Oxford, United Kingdom: Butterworth-Heinemann; 2003.
25. Lin C, Hu PJ, Chen H. Technology implementation management in law enforcement: COPLINK system usability and user acceptance evaluations. Soc Sci Comput Rev 2004, 22:24–36.
26. Šišlák D, Pěchouček M, Volf P, Pavlíček D, Samek J, Mařík V, Losiewicz P. AGENTFLY: towards multi-agent technology in free flight air traffic control. In: Defence Industry Applications of Autonomous Agents and Multi-Agent Systems, 2007, 73–96. Basel: Birkhäuser.
27. He Q, Sycara K, Su Z. A solution to open standard of PKI. In: Australasian Conference on Information Security and Privacy, 13 July, 1998, 99–110. Berlin, Heidelberg: Springer.
28. Hegazy IM, Al-Arif T, Fayed ZT, Faheem HM. A multi-agent based system for intrusion detection. IEEE Potentials 2003, 22:28–31.
29. Goldberg HG, Wong RW. Restructuring transactional data for link analysis in the FinCEN AI system. In: AAAI Fall Symposium, January 1998, 38–46.
30. Aggarwal CC, Zhai C, eds. Mining Text Data. Berlin, Germany: Springer Science & Business Media; 2012.
31. Zhong N, Li Y, Wu ST. Effective pattern discovery for text mining. IEEE Trans Knowl Data Eng 2012, 24:30–44.
32. Al-Zaidy R, Fung B, Youssef AM. Towards discovering criminal communities from textual data. In: Proceedings of the 2011 ACM Symposium on Applied Computing, ACM, 21 March, 2011, 172–177.
33. Tseng YH, Ho ZP, Yang KS, Chen CC. Mining term networks from text collections for crime investigation. Expert Syst Appl 2012, 39:10082–10090.
34. Poelmans J, Van Hulle MM, Viaene S, Elzinga P, Dedene G. Text mining with emergent self organizing maps and multi-dimensional scaling: a comparative study on domestic violence. Appl Soft Comput 2011, 11:3870–3876.
35. Lee R. Automatic information extraction from documents: A tool for intelligence and law enforcement analysts. In: Proceedings of 1998 AAAI Fall Symposium on Artificial Intelligence and Link Analysis, 23 October, 1998. Menlo Park, CA: AAAI Press.
36. Fuller CM, Biros DP, Delen D. An investigation of data and text mining methods for real world deception detection. Expert Syst Appl 2011, 38:8392–8398.
37. Yin D, Xue Z, Hong L, Davison BD, Kontostathis A, Edwards L. Detection of harassment on web 2.0. Proc Content Anal Web 2009, 2:1–7.
38. McGreevy MW. Using Perilog to explore “Decision Making at NASA”.
39. Connell L. NASA Aviation Safety Reporting System (ASRS).
40. Bovbjerg KM. Personal development under market conditions: NLP and the emergence of an ethics of sensitivity based on the idea of the hidden potential of the individual. J Contemp Relig 2011, 26:189–205.
41. Haykin S, Network N. A comprehensive foundation. Neural Netw 2004, 2:41.
42. Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M. Crime data mining: a general framework and some examples. Computer 2004, 37:50–56.
43. Strano M. A neural network applied to criminal psychological profiling: an Italian initiative. Int J Offender Ther Comp Criminol 2004, 48:495–503.
44. Chen H, Chung W, Qin Y, Chau M, Xu JJ, Wang G, Zheng R, Atabakhsh H. Crime data mining: an overview and case studies. In: Proceedings of the 2003 Annual National Conference on Digital Government Research, Digital Government Society of North America, 18 May, 2003, 1–5.
45. Xu JJ, Chen H. Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks. Decis Support Syst 2004, 38:473–487.
46. Brahan JW, Lam KP, Chan H, Leung W. AICAMS: artificial intelligence crime analysis and management system. Knowl Syst 1998, 11:355–361.
47. Helbich M, Hagenauer J, Leitner M, Edwards R. Exploration of unstructured narrative crime reports: an unsupervised neural network and point pattern analysis approach. Cartogr Geogr Inf Sci 2013, 40:326–336.
48. Maren AJ, Harston CT. Pap RM. Handbook of Neural Computing Applications: Academic Press; 2014.
49. Brown DE, Gunderson LF. Using clustering to discover the preferences of computer criminals. IEEE Trans Syst Man Cybern A Syst Hum 2001, 31:311–318.
50. Dahbur K, Muscarello T. Classification system for serial criminal patterns. Artif Intell Law 2003, 11:251–269.
51. Linda O, Vollmer T, Manic M. Neural network based intrusion detection system for critical infrastructures. In: Neural Networks. IJCNN 2009. International Joint Conference, IEEE 14 June, 2009, 1827–1834).
52. Elder JF, Abbott DW. A comparison of leading data mining tools. In: Fourth International Conference on Knowledge Discovery and Data Mining, 28 August, 1998.
53. Veltkamp RC, Tanase M. Content-based image retrieval systems: a survey.
54. Mitchell TM. Machine Learning. Burr Ridge, IL: McGraw Hill; 1997.
55. Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD. Artificial Intelligence: A Modern Approach, vol. 2. Upper Saddle River, NJ: Prentice Hall; 2003.
56. Baumgartner K, Ferrari S, Palermo G. Constructing Bayesian networks for criminal profiling from limited data. Knowl Syst 2008, 21:563–572.
57. Franke K, Srihari SN. Computational forensics: an overview. In: International Workshop on Computational Forensics, 7 August 2008, 1–10. Berlin, Heidelberg: Springer.
58. Nath SV. Crime pattern detection using data mining. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2006 Workshops, IEEE, December 2006, 41–44.
59. Wang T, Rudin C, Wagner D, Sevieri R. Learning to detect patterns of crime. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 23 September, 2013, 515–530. Berlin, Heidelberg: Springer.
60. Ku CH, Iriberri A, Leroy G. Crime information extraction from police and witness narrative reports. In: 2008 I.E. Conference on Technologies for Homeland Security, IEEE, 12 May, 2008, 193–198.
61. Stoffel K, Cotofrei P, Han D. Fuzzy methods for forensic data analysis. In: International Conference of Soft Computing and Pattern Recognition (SoCPaR), IEEE, 7 December, 2010, 23–28.
62. Gupta P, Sharma A, Jindal R. Scalable machine-learning algorithms for big data analytics: a comprehensive review. WIREs Data Min Knowl Discov 2016, 6:194–214.
63. Van Halteren H, Baayen H, Tweedie F, Haverkort M, Neijt A. New machine learning methods demonstrate the existence of a human stylome. J Quant Linguist 2005, 12:65–77.
64. Chau M, Xu JJ, Chen H. Extracting meaningful entities from police narrative reports. In: Proceedings of the 2002 Annual National Conference on Digital Government Research, Digital Government Society of North America, 19 May, 2002, 1–5.
65. Yang CC, Ng TD. Terrorism and crime related weblog social network: link, content analysis and information visualization. In: Intelligence and Security Informatics, IEEE, 23 May, 2007 May 23, 55–58.
66. Kerschbaum F, Schaad A. Privacy-preserving social network analysis for criminal investigations. In: Proceedings of the 7th ACM workshop on Privacy in the electronic society, ACM, 27 October, 2008, 9–14.
67. Smets K, Goethals B, Verdonk B. Automatic vandalism detection in Wikipedia: towards a machine learning approach. In: AAAI workshop on wikipedia and artificial intelligence: an evolving synergy, 13 July, 2008, 43–48.
68. Goldberg HG, Kirkland JD, Lee D, Shyr P, Thakker D. The NASD Securities Observation, New Analysis and Regulation System (SONAR). In: IAAI, 12 August, 2003, 11–18.
69. Gao S, Xu D. Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering. Expert Syst Appl 2009, 36:1493–1504.
70. Wang GA, Chen H, Xu JJ, Atabakhsh H. Automatically detecting criminal identity deception: an adaptive detection algorithm. IEEE Trans Syst Man Cybern A Syst Hum 2006, 36:988–999.
71. Monge AE. Adaptive detection of approximately duplicate database records and the database integration approach to information discovery. Doctoral dissertation, University of California, San Diego, 1997.
72. Helmer GG, Wong JS, Honavar V, Miller L. Intelligent agents for intrusion detection. In: Information Technology Conference, IEEE, 1 September, 1998, 121–124.
73. Fanning K, Cogger KO, Srivastava R. Detection of management fraud: a neural network approach. ISAFM 1995, 4:113–126.
74. Adderley R, Townsley M, Bond J. Use of data mining techniques to model crime scene investigator performance. Knowl Syst 2007, 20:170–176.
75. Lu Y, Luo X, Polgar M, Cao Y. Social network analysis of a criminal hacker community. J Comput Inform Syst 2010, 51:31–41.
76. Burt RS. Models of network structure. Annu Rev Sociol 1980, 6:79–141.
77. Pramanik MI, Zhang W, Lau RY, Li C. A framework for criminal network analysis using big data. In: 13th International Conference on e-Business Engineering (ICEBE), IEEE, 4 November, 2016, 17–23.
78. Popp R, Armour T, Numrych K. Countering terrorism through information technology. Commun ACM 2004, 47:36–43.
79. Zikopoulos P, Eaton C. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. New York: McGraw-Hill Osborne Media; 2011. Chapter 3.
80. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2014, 2:3.
81. Zikopoulos P, Parasuraman K, Deutsch T, Giles J, Corrigan D. Harness the Power of big data. The IBM Big Data Platform; McGraw Hill Professional 2012.
82. Borthakur D. HDFS architecture guide. Hadoop Apache Project; 2008: 53.
83. Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R. Hive: a warehousing solution over a map-reduce framework. Proc VLDB Endowment 2009, 2:1626–1629.
84. Fernández A, del Río S, López V, Bawakid A, del Jesús MJ, Benítez JM, Herrera F. Big data with cloud computing: an insight on the computing environment, MapReduce, and programming frameworks. WIREs Data Min Knowl Discov 2014, 4:380–409.
85. Lakshman A, Malik P. Cassandra: a decentralized structured storage system. ACM SIGOPS Operat Syst Rev 2010, 44:35–40.
86. Floratou A, Patel JM, Shekita EJ, Tata S. Column-oriented storage techniques for MapReduce. Proc VLDB Endowment 2011, 4:419–429.
87. Hunt P, Konar M, Junqueira FP, Reed B. ZooKeeper: Wait-free Coordination for Internet-scale Systems. In: USENIX annual Technical Conference, 23 June, 2010; 8: 9.
88. McCandless M, Hatcher E, Gospodnetic O. Lucene in Action: Covers Apache Lucene 3.0. Greenwich, CT: Manning Publications Co; 2010.
89. Islam M, Huang AK, Battisha M, Chiang M, Srinivasan S, Peters C, Neumann A, Abdelnur A. Oozie: towards a scalable workflow management system for Hadoop. In: Proceedings of the 1st ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies, ACM, 20 May, 2012, 4.
90. Liao SH, Chu PH, Hsiao PY. Data mining techniques and applications–a decade review from 2000 to 2011. Expert Syst Appl 2012, 39:11303–11311.
91. Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. J Assoc Inf Sci Technol 2007, 58:1019–1031.
92. Abbasoğlu MA, Gedik B, Ferhatosmanoğlu H. Aggregate profile clustering for telco analytics. Proc VLDB Endowment 2013, 6:1234–1237.
93. Fan S, Lau RY, Zhao JL. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Res 2015, 2:28–32.
94. Sarvari H, Abozinadah E, Mbaziira A, McCoy D. Constructing and analyzing criminal networks. In: Security and Privacy Workshops (SPW), IEEE, 2014, 84–91.
95. Rogers M. The role of criminal profiling in the computer forensics process. Comput Secur 2003, 22:292–298.
96. Loh S, Wives LK, de Oliveira JP. Concept-based knowledge discovery in texts extracted from the web. ACM SIGKDD Explor Newslett 2000, 2:29–39.
97. Westphal C. Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies. Boca Raton, FL: CRC Press; 2008.
98. McCue C. Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis. Oxford, United Kingdom: Butterworth-Heinemann; 2014.
99. Drewes B. Integration of text and data mining. WIT Trans Inform Commun Technol 2002, 10:28.
100. McCue C. Data Mining and Predictive Analysis. Oxford: Elsevier, Butterworth-Heinemann; 2007.
101. Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, Wirth R. CRISP-DM 1.0 Step-by-step data mining guide.
102. Giraud-Carrier C, Povel O. Characterising data mining software. Intell Data Anal 2003, 7:181–192.
103. Sherman R. Data integration advisor: set the stage with data preparation. DM Rev 2005, 15: 54–55.
104. Ozgul F, Atzenbeck C, Celik A, Erdem Z. Incorporating data sources and methodologies for crime data mining. In: IEEE International Conference on Intelligence and Security Informatics (ISI), IEEE, 2011, 176–180.
105. House W. Big data: seizing opportunities, preserving values (Report for the President), 2014. Washington, DC: Executive Office of the President. [WWW document]. Available at: http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf
106. Piatetsky-Shapiro G. Knowledge discovery in databases: 10 years after. ACM SIGKDD Explor Newslett 2000, 1:59–61.
107. Bollier D, Firestone CM. The promise and peril of big data. Washington, DC: Aspen Institute, Communications and Society Program; 2010.
108. Ramírez-Gallego S, García S, Mouriño-Talín H, Martínez-Rego D, Bolón-Canedo V, Alonso-Betanzos A, Benítez JM, Herrera F. Data discretization: taxonomy and big data challenge. WIREs Data Min Knowl Discov 2016, 6:5–21.
109. Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE Trans Knowl Data Eng 2014, 26:97–107.
110. Sagiroglu S, Sinanc D. Big data: a review. In: International Conference on Collaboration Technologies and Systems (CTS), IEEE, 2013, 42–47.