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Computational techniques to counter terrorism: a systematic survey

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Abstract

Terrorist Network Analysis (TNA) is the field of analyzing and defining the scope of terrorism and researching the countermeasures in order to handle exponentially increasing threats due to ever growing terrorist based activities. This field constitutes several sub-domains such as crawling the data about terrorist attacks/groups, classification, behavioral, and predictive analysis. In this paper we present a systematic review of TNA which includes study of different terrorist groups and attack characteristics, use of online social networks, machine learning techniques and data mining tools in order to counter terrorism. Our survey is divided into three sections of TNA: Data Collection, Analysis Approaches and Future Directions. Each section highlights the major research achievements in order to present active use of research methodology to counter terrorism. Furthermore, the metrics used for TNA analysis have been thoroughly studied and identified. The paper has been written with an intent of providing all the necessary background to the researchers who plan to carry out similar studies in this emerging field of TNA. Our contributions to TNA field are with respect to effective utilization of computational techniques of data mining, machine learning, online social networks, and highlighting the research gaps and challenges in various sub domains.

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References

  1. Abbasi A, Chen H (2005) Analysis to extremist- messages. Intell Syst IEEE 20(5):67–75

    Article  Google Scholar 

  2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proc 20th int conf very large data bases, VLDB. vol 1215

  3. Allanach J, Tu H, Singh S, Willett P, Pattipati K (2004) Detecting, tracking, and counteracting terrorist networks via hidden markov models. In: IEEE Aerospace Conference, pp 3246–3257

  4. Allister D, Ipung HP, Nugroho SA (2010) Text classification techniques used to faciliate cyber terrorism investigation. In: 2nd International conference on advances in computing, control, and telecommunication technologies

  5. Apollo Social Sensing Toolkit (2020) [Online]. Available: http://apollo2.cs.illinois.edu/index.html

  6. Archetti F, Djordjevic D, Giordani I, Sormani R, Tisato F (2014) A reasoning approach for modelling and predicting terroristic attacks in urban environments. In: Security Technology (ICCST), 2014, pp 1–6

  7. Asongu SA, Orim S-MI, Nting RT (2019) Terrorism and social media: global evidence. J Global Inf Technol Manag 22.3:208–228

    Google Scholar 

  8. AutoMap: Project — CASOS (2016) [Online]. Available: http://www.casos.cs.cmu.edu/projects/automap/. [Accessed: 08 May 2016]

  9. Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition. IEEE Trans Syst Man Cybern Part B 29(6):778–785

    Article  Google Scholar 

  10. Berzinji A, Abdullah FS, Kakei AH (2003) Analysis of terrorist groups on facebook. In: 2013 European intelligence and security informatics conference analysis, p 7695

  11. Best DM, Northwest P (2015) Clique: Situational awareness. IT Prof 117(August):66–68

    Article  Google Scholar 

  12. Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: A comparative study. Decis Support Syst 50(3):602–613

    Article  Google Scholar 

  13. BioWar: Project — CASOS (2016) [Online]. Available: http://www.casos.cs.cmu.edu/projects/biowar/. [Accessed: 08 May 2016]

  14. Brajawidagda U, Reddick CG, Chatfield AT (2016) Social media and urban resilience: A case study of the 2016 Jakarta terror attack. In: Proceedings of the 17th International digital government research conference on digital government research

  15. Brynielsson J, Sharma R (2015) Detectability of low-rate HTTP server DoS attacks using spectral analysis. In: 2015 IEEE/ACM International conference on advances in social networks analysis and mining, pp 954–961

  16. Burke R, Mobasher B, williams C, Bhaumik R (2006) Classification features for attack detection in collaborative recommender systems. In: KDD ’06 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 542–547

  17. Cao L, Member S, Zhao Y (2008) Mining Impact-Targeted activity patterns in imbalanced data. IEEE Trans Knowl Data Eng 20(8):1053–1066

    Article  Google Scholar 

  18. Chen J, Li B (2009) Random forest for relational classification with application to terrorist profiling. In: Granular computing, 2009, GRC ’09. IEEE International Conference, pp 630–633

  19. Chen J, Xu J, Chen P, Ding G, Lax RF, Marx BD (2008) Fuzzy clustering and iterative relational classification for terrorist profiling. In: Granular Computing, 2008. GrC 2008. IEEE International Conference on, pp 142–147

  20. Coffman TR, Suite JR, Marcus SE (2004) Dynamic classification of groups through social network analysis and HMMsl. 2. In: IEEE Aerospace conference proceedings dynamic classification of groups through social network analysis and HMMsl.2 Thayne R. Coffman 2 1” Century technologies, Inc. 11675 Jollyville Rd Suite 300 Austin, TX 78759 tcoffman@2 1 technologies.com 5 12-342-00 I , pp 1–9

  21. Dark Web Forums (2016) [Online]. Available: http://www.azsecure-data.org/dark-web-forums.html. [Accessed: 08 May 2016]

  22. Dark Web Project (2016) [Online]. Available: http://www.azsecure-data.org/about.html. [Accessed: 08 Feb 2016]

  23. David Liben-Nowell JK (2013) The link prediction problem for social networks. Int Rev Res Open Distance Learn 14(4):90–103

    Google Scholar 

  24. Desmarais BA, Cranmer SJ, Hill C, Carolina N (2011) Forecasting the locational dynamics of transnational terrorism:, a network analytic approach. In: European intelligence and security informatics conference, pp 171–177

  25. Deylami HA (2015) Link prediction in social networks using hierarchical community detection. In: IKT2015 7th International conference on information and knowledge technology, pp 1–5

  26. Dhote Y, Mishra N (2013) Survey and analysis of temporal link prediction in online social networks. In: International Conference ondvances in Computing, Communications and Informatics (ICACCI), pp 1178–1183

  27. Dickerson JP (2011) Dealing with Lashkar-e-Taiba: A multi-player game-theoretic perspective. In: 2011 European intelligence and security informatics conference, pp 354–359

  28. Dimensions AT (2015) Security QoS profiling against cyber terrorism in airport network systems. In: 2015 International Conference on Cyberspace Governance (CYBER-ABUJA), pp 241–251

  29. Duman E, Ozcelik MH (2011) Expert Systems with Applications Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst Appl 38(10):13057–13063

    Article  Google Scholar 

  30. Duo-yong S (2011). In: Intelligence and Security Informatics (ISI), 2011 IEEE International Conference on, no 70973138, pp 373–378

  31. DyNetML— CASOS (2016) [Online]. Available: http://www.casos.cs.cmu.edu/projects/dynetml/. [Accessed: 08 May 2016]

  32. Engene JO (2006) The data set Terrorism in Western Europe: Events Data, or TWEED for short, was constructed and collected for the purpose of analysing patterns of terrorism in Western Europe as related to historical and structural preconditions (see Jan Oskar Engene: Eur. [Online]. Available: http://folk.uib.no/sspje/tweed.htm

  33. Fire M, Tenenboim L, Lesser O, Puzis R, Rokach L, Elovici Y (2011) Link prediction in social networks using computationally efficient topological features. Proceedings - 2011 IEEE Int Conf Privacy, Secur Risk Trust IEEE Int Conf Soc Comput PASSAT/SocialCom 2011:73–80

    Google Scholar 

  34. Fu J, Sun D, Chai J (2012) Multi-factor analysis of terrorist activities based on social network. In: 2012 5th International conference on business intelligence and financial engineering multi-factor, pp 476–480

  35. Goldstein BH (2006) Modeling, IEEE Spectr, no September, pp 26–34

  36. Goyal T, Saini JK, Bansal D (2019) Analyzing behavior of ISIS and Al-Qaeda using association rule mining. In: Proceedings of 2nd International conference on communication, computing and networking, Springer, Singapore

  37. Guohui LI, Song LU, Xudong C, Hui Y, Heping Z (2014) Study on correlation factors that influence terrorist attack fatalities using Global Terrorism Database. In: 2014 International symposium on safety science and technology study, pp 698–707

  38. Haglich P, Rouff C (2010) Detecting emergence in social networks. In: IEEE international conference on social computing/IEEE international conference on privacy, security, risk and trust, pp 693–696

  39. Hogan B (2011) Visualizing and interpreting facebook networks

  40. Hsiao H, Lin CS, Chang S (2009) Constructing an ARP attack detection system with SNMP traffic data mining. In: Proceedings of the 11th international conference on electronic commerce. ACM, pp 341–345

  41. Huang Z, Chen H, Yu T, Sheng H, Luo Z (2009) Semantic text mining with linked data. In: 2009 5th International joint conference on INC, IMS and IDC, pp 338–343

  42. Huillier GL, Huillier GL, Alvarez H, Aguiler F, Aguilera F (2010) Topic-based social network analysis for virtual communities of interests in the dark web topic-based social network analysis for virtual communities interests in the dark web. In: ACM SIGKDD Workshop on intelligence and security informatics, p9, pp 66–73

  43. International Terrorism: Attributes of Terrorist Events (ITERATE) (2016) [Online]. Available: http://www.icpsr.umich.edu/icpsrweb/DSDR/studies/07947#datasetsSection. [Accessed: 08 Feb 2016]

  44. JDK (2016) [Online]. Available: http://www.oracle.com/technetwork/java/javase/downloads/index.html. [Accessed: 07 May 2016]

  45. Jan G (2011) Visual investigation of similarities in global terrorism database by means of synthetic social networks. In: Computational Aspects of Social Networks (CASon), 2011 International Conference on, IEEE, 2011, pp 255–260

  46. Jayaweera I, Sajeewa C, Liyanage S, Wijewardane T, Perera I, Wijayasiri A (2015) Crime analytics: Analysis of crimes through newspaper articles. In: 2015 Moratuwa Eng Res Conf, pp 277–282

  47. Jin L, Diego S, Chen Y (2013) Understanding user behavior in online social networks: A survey, no September, pp 144–150

  48. Johnson CW (2005) Applying the lessons of the attack on the world trade center, 11th september 2001, to the design and use of interactive evacuation simulations. In: Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 2005, pp 651–660

  49. Katipally R, Cui X (2010) Multistage attack detection system for network administrators using data mining, pp 0–3

  50. Kaur A, Saini JK, Bansal D (2019) Detecting radical text over online media using deep learning. arXiv:1907.12368

  51. Kengpol A, Neungrit P (2014) A decision support methodology with risk assessment on prediction of terrorism insurgency distribution range radius and elapsing time: An empirical case study in Thailand. Comput Ind Eng 75:55–67

    Article  Google Scholar 

  52. Klausen J (2016) The role of social networks in the evolution of Al Qaeda-inspired violent extremism in the United States, 1990-2015 Brandeis University

  53. Krause L, Lehman L, Wheeler A, Drive S (2003) A family of agent based models

  54. Lautenschlager J, Ruvinsky A, Warfield I, Kettler B (2015) Group profiling automation for crime and terrorism (GPACT). Procedia Manuf 3(Ahfe):3933–3940

    Article  Google Scholar 

  55. Liu Z, Xin L, Peng P (2011) Comparison of two mathematical models for predicting the fallout hazard from terrorist nuclear detonations. In: International conference on electronic & mechanical engineering and information technology, pp 4007–4009

  56. Moon I, Carley KM (2007) Modeling and networks in social and Geospatial dimensions

  57. Mannes A, Michael M, Pate A, Sliva A, Subrahmanian VS, Wilkenfeld J (2008) Stochastic opponent modeling agents: A case study with hezbollah, Soc Comput Behav Model Predict no. September 2002, pp 37–45

  58. Mannes A, Shakarian J, Sliva A, Subrahmanian VS (2011) A computationally-enabled analysis of lashkar-e-taiba attacks in jammu & kashmir. In: 2011 European intelligence and security informatics conference, pp 224–229

  59. Mason R, Mcinnis B, Dalal S (2012) Machine learning for the automatic identification of terrorist incidents in worldwide news media, pp 84–89

  60. Mathieu B, Truong P, You W, Labs O (2012) Information-centric networking: A natural design for social network applications, no July, pp 44–51

  61. McKerlich R, Ives C, McGreal R (2013) Uncovering the darkweb: A case study of jihad on theWeb. Int Rev Res Open Distance Learn 14(4):90–103

    Google Scholar 

  62. Miller RR (2002) Viewpoint Information Management in, vol 45, no 9, pp 31–33

  63. Mitchell TM (1997) Machine learning, no 1

  64. NODEXL (2007) [Online]. Available: http://www.smrfoundation.org/tools/

  65. National Consortium for the Study of Terrorism and Responses to Terrorism (START) (2015) GTD Global terrorism database, START. p 63

  66. Ngai EWT, Hu Y, Wong YH, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decis Support Syst 50(3):559–569

    Article  Google Scholar 

  67. Nguyen LT, Wu P, Chan W, Field M (2012) Predicting collective sentiment dynamics from time-series social media. In: WISDOM ’12 Proceedings of the 1st international workshop on issues of sentiment discovery and opinion mining

  68. ORA-LITE: Software — CASOS (2016) [Online]. Available: http://www.casos.cs.cmu.edu/projects/ora/software.php. [Accessed: 08 May 2016]

  69. Ozgul F, Atzenbeck C, Erdem Z (2011) How much similar are terrorists networks of Istanbul?. In: 2011 International conference on advances in social networks analysis and mining how, pp 468–472

  70. Ozgul F, Erdem Z, Bowerman C (2009) Prediction of past unsolved terrorist attacks, pp 37–42

  71. PHP: Hypertext Preprocessor (2016) [Online]. Available: https://secure.php.net/. [Accessed: 07 May 2016]

  72. Pagán JV (2010) Improving the classification of terrorist attacks a study on data pre-processing for mining the global terrorism database. In: 2010 2nd International Conference on Software Technology and Engineering(ICSTE) Improving, pp 104–110

  73. Pavel A (2005) Responding to terrorist attacks and natural disasters: a case study using simulation. In: Winter simulation conference, pp 886–894

  74. Projects - Construct — CASOS (2016) [Online]. Available: http://www.casos.cs.cmu.edu/projects/construct/. [Accessed: 08 May 2016]

  75. Projects - OrgAhead — CASOS (2016) [Online]. Available: http://www.casos.cs.cmu.edu/projects/OrgAhead/. [Accessed: 08 May 2016]

  76. Python Programming Language (2016) [Online]. Available: https://www.python.org/. [Accessed: 07 May 2016]

  77. R (2016) The R project for statistical computing. [Online]. Available: https://www.r-project.org/. [Accessed: 07 May 2016]

  78. R. C., DARPA (2019) RDWTI — RAND [Online]. Available: http://www.rand.org/nsrd/projects/terrorism-incidents.html

  79. Rejaie R, Torkjazi M, Valafar M, Willinger W (2010) Sizing up online social networks. Network, IEEE 24(5):32–37

    Article  Google Scholar 

  80. START (2005) MAROB. [Online]. Available: http://www.cidcm.umd.edu/mar/mar_data.asp

  81. START (2020) Global Terrorism Database. [Online]. Available: http://www.start.umd.edu/gtd/

  82. Sang J, Deng Z, Lu D, Xu C (2015) Cross-OSN user modeling by homogeneous behavior quantification and local social regularization. IEEE Trans Multimed 17(12):2259–2270

    Article  Google Scholar 

  83. Sardarnia K, Safizadeh R (2019) The internet and its potentials for networking and identity seeking: A study on ISIS. Terror Political Violence 31.6:1266–1283

    Article  Google Scholar 

  84. Scanlon JR, Gerber MS (2014) Automatic detection of cyber-recruitment by violent extremists. Secur Inform 3(1):1–10

    Article  Google Scholar 

  85. Schreck T, Keim D (2013) Visual analysis of social media data, pp 68–75

  86. Serra E, Subrahmanian VS (2014) A survey of quantitative models of terror group behavior and an analysis of strategic disclosure of behavioral models. IEEE Trans Comput Soc Syst 1(1): 66–88

    Article  Google Scholar 

  87. Shen H, Member S, Li Z, Liu J, Grant JE (2015) Knowledge sharing in the online social network of Yahoo! Answers and Its Implications. IEEE Trans Comput 64(6):1715–1728

    MathSciNet  Google Scholar 

  88. Siegrist D (2000) Advanced information technology to counter biological terrorism. ACM SIGBIO Newsletter 20.2:2–7

    Article  Google Scholar 

  89. Skillicorn DB (2015) Empirical assessment of al qaeda, isis, and taliban propaganda. In: Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on, pp 61–66

  90. Skillicorn DB, Spezzano F, Subrahmanian VS (2014) Understanding south asian violent extremist group-group interactions, no Asonam, pp 687–694

  91. South Asia Terrorism Portal (SATP) (2016) [Online]. Available: http://www.satp.org/. [Accessed: 08 Feb 2016]

  92. Spezzano F, Mannes A (2013) STONE: Shaping terrorist organizational network efficiency. In: 2013 IEEE/ACM International conference on advances in social networks analysis and mining STONE, pp 348–355

  93. Su P, Mao W, Zeng D, Zhao H (2012) Mining actionable behavioral rules. Decis Support Syst 54(1):142–152

    Article  Google Scholar 

  94. Subrahmanian EVS (2012) Handbook of computational approaches to counter terrorism, Springer Science & Business Media

  95. Suicide Attack Database (SAD) (2016) [Online]. Available: https://cpostdata.uchicago.edu/search_new.php. [Accessed: 08 Feb 2016]

  96. Taquechel E, Guard USC (2010) Layered defense: Modeling terrorist transfer threat networks and optimizing network risk reduction, no December, pp 30–35

  97. The crawler4j Open Source Project on Open Hub (2016) [Online]. Available: https://www.openhub.net/p/crawler4j. [Accessed: 07 May 2016]

  98. Thuraisingham B (2004) Data mining for counter-terrorism. Data Mining:, Next Generation Challenges and Future Directions, pp 157–183

  99. UCINET Software (2016) [Online]. Available: https://sites.google.com/site/ucinetsoftware/home. [Accessed: 08 May 2016]

  100. Valenzuela ML, Feng C, Momen F, Rozenblit JW (2010) A non-numerical predictive model for asymmetric analysis

  101. Visualization AW, Lee J (2008) Exploring global terrorism data: A web-based visualization of temporal data, vol 15, no 2, pp 7–14

  102. Wang P (2014) A tale of three social networks, pp 10–15

  103. Wang J (2014) A novel method of centrality in terrorist network, pp 144–149

  104. Wang T, Liao G (2014) A review of link prediction in social networks. In: International conference on management of e-commerce and e-government, pp 149–152

  105. Weil T (2015) Happened to sony happen to us?, no April

  106. Weinstein C, Campbell W, Delaney B, Leary GO, Street W (2009) Modeling and detection techniques for counter-terror social network analysis and intent recognition

  107. Wiil UK, Gniadek J, Memon N (2010) Measuring link importance in terrorist networks. In: 2010 International conference on advances in social networks analysis and mining. IEEE, pp 225–232

  108. Worldwide Incident Tracking System (WITS) (2004) [Online]. Available: https://esoc.princeton.edu/files/global-terrorism-database-worldwide-incident-tracking-system

  109. Xiong H, Wu J, Chen J (2009) K-means clustering versus validation measures: a data-distribution perspective. IEEE Trans Syst, Man, Cybern Part B 39(2):318–331

    Article  Google Scholar 

  110. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678. [Online]. Available: https://doi.org/10.1109/TNN.2005.845141

    Article  Google Scholar 

  111. Ze L, Duo-yong S, Shu-quan G, Bo L (2014) Detecting Key Individuals in Terrorist Network Based on FANP Model. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp 724–727

  112. Zhou Y, Qin J, Reid E, Lai G, Chen H (2005) Studying the presence of terrorism on the web: A knowledge portal approach. In: Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries. ACM, 2005, p 402

  113. (2017). https://www.start.umd.edu/gtd/images/START_GlobalTerrorismDatabase_TerroristAttacksConcentrationIntensityMap_45Years.png [Date Accessed 23 Nov 2017]

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Funding

This work was partially supported by Cyber Security Research Centre, Punjab Engineering College (Deemed to be University), Chandigarh, India.The author Jaspal K Saini is grateful to Visvesvaraya PhD scheme for Electronics and IT for funding this research.

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  1. School of Computing, IIIT Una, Una, Himachal Pradesh, India

    Jaspal Kaur Saini

  2. Punjab Engineering College (Deemed To Be University), CSRC, Chandigarh, India

    Divya Bansal

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Table 13 List of Acronyms
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Saini, J.K., Bansal, D. Computational techniques to counter terrorism: a systematic survey. Multimed Tools Appl 83, 1189–1214 (2024). https://doi.org/10.1007/s11042-023-15545-0

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