64d37246a59d6.pdf
DOI:
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Countering terrorism online with artificial intelligence An Overview for Law Enforcement and Counter-Terrorism Agencies in South Asia and South-East Asia https://unicri.it/News/-Countering-Terrorism-Online-with-Artificial-Intelligence
Kathleen McKendrick International Security Department | August 2019 Artificial Intelligence Prediction and Counterterrorism https://www.chathamhouse.org/sites/default/files/2019-08-07-AICounterterrorism.pdf
Kurth Cronin, A. (2004), ‘Sources of Contemporary Terrorism’, in Kurth Cronin, A. and Lundes, J. (eds) (2004), Attacking Terrorism: Elements of a Grand Strategy, Washington, DC: Georgetown University Press, 2004, p.33.
Monaco, L (2017) ‘Preventing the Next Attack; A Strategy for the War on Terrorism’ Foreign Affairs 96(6), pp.23–29.
Van Puyvelde, D., Coulthart, S. and Hossain, M. S. (2017), ‘Beyond the Buzzword: big data and national security decision-making’, International Affairs, 93(26), pp. 1397–1416.
Anderson, D. (2017), Attacks in London and Manchester March–June 2017, independent assessment of M15 and police internal reviews, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/66468 2/
Elias, B. (2014), Risk-Based Approaches to Airline Passenger Screening, Congressional Research Service Report, 31 March 2014, https://www.hsdl.org/?view&did=752251
Akhgar, B., Saathoff, G. B., Arabnia, H., Hill, R., Staniforth, A. and Bayerl, P. (2015), Application of Big Data for National Security, Oxford: ButterworthHeinemann.
Van Puyvelde et al. (2017), ‘Beyond the Buzzword: big data and national security decision-making’, p. 1398.
Weaver, M. (2016), ‘Search for UK jihadi in Isis video to use voice and vein recognition software’, Guardian, 4 January 2016, https://www.theguardian.com/ world/2016/jan/04/isis-video-uk-jihadi-voice-vein-recognition-software
Stewart, H. (2017), ‘May calls on internet firms to remove extremist content within two hours’, Guardian, 20 September 2017, https://www.theguardian.com/uknews/2017/sep/19/theresa-may-will-tell-internet-firms-to-tackle-extremist-content
Titcomb, J. (2017), ‘Internet giants insist they are tackling terrorism, but it is right to demand more’, Telegraph, 17 October 2017; Chazan, G.(2018), ‘Twitter suspends top AfD MP under new German hate speech law’, Financial Times, 2 January 2018, https://www.ft.com/content/19f89fb2-efc7-11e7-b220-857e26d1aca4
Bickert, M. (2017), ‘Hard Questions: How We Counter Terrorism’, Facebook Newsroom, https://newsroom.fb.com/news/2017/06/how-we-counterterrorism/
Murphi, M. (2018), ‘Facebook pays terror victims to talk down extremists on Messenger’, Telegraph, 27 February 2018, https://www.telegraph.co.uk/ technology/2018/02/27/facebook-funds-terror-victims-talk-extremists-messenger/
Subrahmanian, V. S. (ed.) (2013), Handbook of Computational Approaches to Counterterrorism, New York: Springer
Dickerson, J. P., Simari, G. I. and Subrahmanian, V. S. (2013), ‘Using Temporal Probabilistic Rules to Learn Group Behaviour’, in Subrahmanian, V. S. (ed.) (2013), Handbook of Computational Approaches to Counterterrorism, New York: Springer.
Ramakrishnan, N. et al. (2014), ‘Beating the News’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators, New York:KDD, 14 August 2014, https://people.cs.vt.edu/naren/papers/kddindg1572-ramakrishnan.pdf
Jigsaw (2018), ‘How can technology make people in the world safer?’, https://jigsaw.google.com/vision/
Jigsaw (2016). ‘The Redirect Method’, https://www.redirectmethod.org
Xiaohui Pan Quantitative Analysis and Prediction of Global Terrorist Attacks Based on Machine Learning Scientific Programming Volume 2021, Article ID 7890923, 15 pages https://doi.org/10.1155/2021/7890923
J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, no. Feb, pp. 281–305, 2012.
Ding F, Ge Q, Jiang D, Fu J, Hao M. Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach. PloS One 2017;12(6):e0179057
Gao Y, Wang X, Chen Q, Guo Y, Yang Q, Yang K, Fang T. Suspects prediction towards terrorist attacks based on machine learning. In: 2019 5th International Conference on Big Data and Information Analytics (BigDIA). IEEE; 2019. p. 126–31.
Meng Xi, Nie Lingyu, Song Jiapeng Big data-based prediction of terrorist attacks Computers& Electrical Engineering,volume 77,July 2019,p.120-127
Khorshid MM, Abou-El-Enien TH, Soliman GM. Hybrid classification algorithms for terrorism prediction in middle east and north africa. Int J Emerging Trends Technol Computer Sci 2015;4(3):23–9.
Zhang X, Jin M, Fu J, Hao M, Yu C, Xie X. On the risk assessment of terrorist attacks coupled with multi-source factors. ISPRS Int J Geo-Inform 2018;7 (9):354.
Firas Saidi, Zouheir Trabelsi A hybrid deep learning-based framework for future terrorist activities modeling and prediction Egyptian Informatics Journal 23 (2022) 437–446
Bridgelall, Raj. 2022. An Application of Natural Language Processing to Classify What Terrorists Say They Want. Social Sciences 11: 23. https://doi.org/10.3390/socsci11010023
W. Guo, K. Gleditsch, and A. Wilson, “Retool AI to forecast and limit wars” Nature, vol. 562, no. 7727, pp. 331–333, 2018.