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Prediction of Unknown Terrorist Group Names Responsible for Attacks in Turkey

Year 2022, Volume: 5 Issue: 3, 257 - 268, 31.12.2022

Ibrahim A. Fadel Cemil Öz

https://doi.org/10.35377/saucis...879855

Abstract

In this paper, the dataset of real incidents that occurred in Turkey between 2013 and 2017 and are regarded as acts of terrorism without any doubt according to Global Terrorism Database (GTD) are used to predict the group names responsible for unknown attacks. Principal Component Analysis (PCA) technique was used for feature selection. A novel voting method between five classification algorithms such as Random Forests, Logistic Regression, AdaBoost, Neural Network, and Support Vector Machine was used to predict the names. The results clearly demonstrate that the classification accuracy of all classifiers studied in this paper improved when PCA was used to select features as compared to selecting features without using PCA. The prediction of terrorist group names with PCA based feature reduction and the original features is carried out and the results are compared.

Keywords

Prediction Classification GTD dataset PCA

References

  • [1] C.C. Aggarwal, Data Classification Algorithms and Applications. London, England, CRC Press Taylor & Francis Group, 2015.
  • [2] A. Babakura, M. N. Sulaiman and M. A. Yusut, “Improved method of classification algorithms for crime prediction”. Proc. - 2014 Int. Symposium on Biometrics and Security Tech., ISBAST 2014, Kuala Lumpur, Malaysia, 26-27, August 2014.
  • [3] BBC News, “Istanbul new year Reina nightclub attack leaves 39 dead,” 2018. [Online]. Available: https://www.bbc.com/news/world-europe-38481521 [Accessed: 09-Dec-2018].
  • [4] E. S. Chris, The Psychology of Terrorism: Theoretical understandings and perspectives. Volume III, Lonon, England, Praeger Publishers, 2002.
  • [5] Institute for Economics & Peace, Global terrorism index 2018. Measuring the impact of terrorism. Sydney, Australia,2018.
  • [6] J. Feng, H. Xu, S. Mannor and S. Yan, “Robust Logistic Regression and Classification,” Proc. - 27th Inter. Conf. on Neural Info. Processing Syst. (NIPS), Montréal, Canada, 08-13 December 2014.
  • [7] F. Gohar, W. Haider and U. Qamar, “Terrorist Group Prediction Using Data Classification,” Proc. – Inter. Conf. on Artificial Intelligence and Pattern Recognition, Kuala Lumpur, Malaysia, 17-19 November 2014.
  • [8] GTD, “About the GTD,” 2019 [Online]. Available: https://www.start.umd.edu/gtd/about/ [Accessed: 09-Jan-2019].
  • [9] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2nd ed. New York, USA, Springer, 2008.
  • [10] T. Howley, M. G. Madden, M. L. O’Connell, and A. G. Ryder, “The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data,” Knowledge-Based Syst., 19(5), 363–370, 2006.
  • [11] Hurriyet, “Kahvehaneye 58 hain kurşun - Son Dakika Haberler,” 2018. [Online]. Available: http://www.hurriyet.com.tr/gundem/kahvehaneye-58-hain-kursun-40048592 [Accessed: 09-Dec-2018].
  • [12] I. T. Jolliffe, Principal Component Analysis. 2nd ed. New York, USA, Springer, 2002.
  • [13] T. Kim, D. Park, D. woo, T. Jeong and S. Min, “Multi-class Classifier-Based Adaboost Algorithm,” Proc. - The Secd. Sino-foreign-interchange conf. on Intelligent Science and Intelligent Data Engineering, Xi'an, China, 23 October 2011.
  • [14] Z. C. Lipton, C. Elkan and B. Narayanaswamy, “Optimal Thresholding of Classifiers to Maximize F1 Measure,” Proc. - European Conf. on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Nancy, France, 15-19 September 2014.
  • [15] A. M. Martinez and A. C. Kak, “PCA versus LDA,”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 228–233, 2001.
  • [16] T. Feakin, “Terror, Security, and Money: Balancing the Risks, Benefits, and Costs of Homeland Security,” The RUSI Journal, 157(4) 99, 2012.
  • [17] F. Ozgul, Z. Erdem and C. Bowerman, “Prediction of past unsolved terrorist attacks,” Proc. - 2009 IEEE Inter. Conf. on Intelligence and Security Informatics (ISI 2009), Dallas/TX, USA, 26 June 2009.
  • [18] D. M. W. Powers, Evaluatıon: From Precısıon, Recall And F-Measure To Roc, Informedness. Markedness & Correlatıon. Journal of Machine Learning Technologies, 2(1), 37–63. 2011.
  • [19] A. Sachan and D. Roy, “TGPM: Terrorist Group Prediction Model for Counter Terrorism,” Inter. Journal of Comp. Appl., 44(10), 49–52. 2012.
  • [20] M. Shermila, A. B. Bellarmine and N. Santiago, “Identity using Machine Learning Approach,” Proc. - 2nd Inter. Conf. on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 11-12,May 2018.
  • [21] START, “Global terrorism database codebook: Inclusion criteria and variables 2018,” 2018. [Online]. Available: https://www.start.umd.edu/gtd/downloads/Codebook.pdf. [Accessed: 02-Nov-2018].
  • [22] D. Talreja, J. Nagaraj, N. J. Varsha and K. Mahesh, “Terrorism analytics: Learning to predict the perpetrator,” Proc. - 2017 Inter. Conf. on Advances in Computing, Communications and Informatics (ICACCI 2017), Mangalore India, 13, September 2017
  • [23] Institute for Economics & Peace. Global terrorism index 2019. Measuring the impact of terrorism. Sydney, Australia, 2019.
  • [24] G. M. Tolan and O. S. Soliman, “An Experimental Study of Classification Algorithms for Terrorism Prediction,” Inter. Journal of Knowledge Engineering-IACSIT, 1(2), 107–112. 2015.

Year 2022, Volume: 5 Issue: 3, 257 - 268, 31.12.2022

Ibrahim A. Fadel Cemil Öz

https://doi.org/10.35377/saucis...879855

Abstract

References

  • [1] C.C. Aggarwal, Data Classification Algorithms and Applications. London, England, CRC Press Taylor & Francis Group, 2015.
  • [2] A. Babakura, M. N. Sulaiman and M. A. Yusut, “Improved method of classification algorithms for crime prediction”. Proc. - 2014 Int. Symposium on Biometrics and Security Tech., ISBAST 2014, Kuala Lumpur, Malaysia, 26-27, August 2014.
  • [3] BBC News, “Istanbul new year Reina nightclub attack leaves 39 dead,” 2018. [Online]. Available: https://www.bbc.com/news/world-europe-38481521 [Accessed: 09-Dec-2018].
  • [4] E. S. Chris, The Psychology of Terrorism: Theoretical understandings and perspectives. Volume III, Lonon, England, Praeger Publishers, 2002.
  • [5] Institute for Economics & Peace, Global terrorism index 2018. Measuring the impact of terrorism. Sydney, Australia,2018.
  • [6] J. Feng, H. Xu, S. Mannor and S. Yan, “Robust Logistic Regression and Classification,” Proc. - 27th Inter. Conf. on Neural Info. Processing Syst. (NIPS), Montréal, Canada, 08-13 December 2014.
  • [7] F. Gohar, W. Haider and U. Qamar, “Terrorist Group Prediction Using Data Classification,” Proc. – Inter. Conf. on Artificial Intelligence and Pattern Recognition, Kuala Lumpur, Malaysia, 17-19 November 2014.
  • [8] GTD, “About the GTD,” 2019 [Online]. Available: https://www.start.umd.edu/gtd/about/ [Accessed: 09-Jan-2019].
  • [9] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2nd ed. New York, USA, Springer, 2008.
  • [10] T. Howley, M. G. Madden, M. L. O’Connell, and A. G. Ryder, “The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data,” Knowledge-Based Syst., 19(5), 363–370, 2006.
  • [11] Hurriyet, “Kahvehaneye 58 hain kurşun - Son Dakika Haberler,” 2018. [Online]. Available: http://www.hurriyet.com.tr/gundem/kahvehaneye-58-hain-kursun-40048592 [Accessed: 09-Dec-2018].
  • [12] I. T. Jolliffe, Principal Component Analysis. 2nd ed. New York, USA, Springer, 2002.
  • [13] T. Kim, D. Park, D. woo, T. Jeong and S. Min, “Multi-class Classifier-Based Adaboost Algorithm,” Proc. - The Secd. Sino-foreign-interchange conf. on Intelligent Science and Intelligent Data Engineering, Xi'an, China, 23 October 2011.
  • [14] Z. C. Lipton, C. Elkan and B. Narayanaswamy, “Optimal Thresholding of Classifiers to Maximize F1 Measure,” Proc. - European Conf. on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Nancy, France, 15-19 September 2014.
  • [15] A. M. Martinez and A. C. Kak, “PCA versus LDA,”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 228–233, 2001.
  • [16] T. Feakin, “Terror, Security, and Money: Balancing the Risks, Benefits, and Costs of Homeland Security,” The RUSI Journal, 157(4) 99, 2012.
  • [17] F. Ozgul, Z. Erdem and C. Bowerman, “Prediction of past unsolved terrorist attacks,” Proc. - 2009 IEEE Inter. Conf. on Intelligence and Security Informatics (ISI 2009), Dallas/TX, USA, 26 June 2009.
  • [18] D. M. W. Powers, Evaluatıon: From Precısıon, Recall And F-Measure To Roc, Informedness. Markedness & Correlatıon. Journal of Machine Learning Technologies, 2(1), 37–63. 2011.
  • [19] A. Sachan and D. Roy, “TGPM: Terrorist Group Prediction Model for Counter Terrorism,” Inter. Journal of Comp. Appl., 44(10), 49–52. 2012.
  • [20] M. Shermila, A. B. Bellarmine and N. Santiago, “Identity using Machine Learning Approach,” Proc. - 2nd Inter. Conf. on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 11-12,May 2018.
  • [21] START, “Global terrorism database codebook: Inclusion criteria and variables 2018,” 2018. [Online]. Available: https://www.start.umd.edu/gtd/downloads/Codebook.pdf. [Accessed: 02-Nov-2018].
  • [22] D. Talreja, J. Nagaraj, N. J. Varsha and K. Mahesh, “Terrorism analytics: Learning to predict the perpetrator,” Proc. - 2017 Inter. Conf. on Advances in Computing, Communications and Informatics (ICACCI 2017), Mangalore India, 13, September 2017
  • [23] Institute for Economics & Peace. Global terrorism index 2019. Measuring the impact of terrorism. Sydney, Australia, 2019.
  • [24] G. M. Tolan and O. S. Soliman, “An Experimental Study of Classification Algorithms for Terrorism Prediction,” Inter. Journal of Knowledge Engineering-IACSIT, 1(2), 107–112. 2015.
There are 24 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Ibrahim A. Fadel 0000-0003-4726-7805

Cemil Öz 0000-0001-9742-6021

Publication Date December 31, 2022
Submission Date February 13, 2021
Acceptance Date August 23, 2022
Published in Issue Year 2022Volume: 5 Issue: 3

Cite

IEEE I. A. Fadel and C. Öz, “Prediction of Unknown Terrorist Group Names Responsible for Attacks in Turkey”, SAUCIS, vol. 5, no. 3, pp. 257–268, 2022, doi: 10.35377/saucis...879855.

Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science      28938