Целью данного аналитического обзора является оценка современного практического применения искусственного интеллекта (ИИ) для борьбы с терроризмом в Интернете, что считается наибольшей угрозой безопасности в Центральной Азии. Автор показывает актуальные направления и модели ИИ, успешность их применения в этой области в качестве инструмента, который может обрабатывать огромные объемы данных и обнаруживать в них скрытые закономерности. Обзор даёт чёткое доказательство того, что искусственный интеллект является эффективным инструментом для анализа и прогнозирования различных механизмов, особенностей террористических атак в мировой практике борьбы с терроризмом, в том числе и в регионе Центральной Азии.
Целью данного аналитического обзора является оценка современного практического применения искусственного интеллекта (ИИ) для борьбы с терроризмом в Интернете, что считается наибольшей угрозой безопасности в Центральной Азии. Автор показывает актуальные направления и модели ИИ, успешность их применения в этой области в качестве инструмента, который может обрабатывать огромные объемы данных и обнаруживать в них скрытые закономерности. Обзор даёт чёткое доказательство того, что искусственный интеллект является эффективным инструментом для анализа и прогнозирования различных механизмов, особенностей террористических атак в мировой практике борьбы с терроризмом, в том числе и в регионе Центральной Азии.
Ushbu tahliliy sharhdan ko‘zlangan maqsad Markaziy Osiyo mintaqasida xavfsizlikka eng katta tahdid hisoblangan internetda terrorizmga qarshi kurashishda sun’iy intellektning amaliy qo‘llanilishini baholashdan iborat. Muallif sun’iy intellektning hozirgi tendentsiyalari va modellarini, ularni katta hajmdagi ma’lumotlarni qayta ishlash va ulardagi yashirin qonunlarni kashf eta oladigan vosita sifatida ushbu sohada qo‘llash muvaffaqiyatini ko‘rsatadi. Sharhda sun’iy intellekt terrorizmga qarshi kurashning jahon amaliyotida, jumladan, Markaziy Osiyo mintaqasida terrorchilik xurujlarining turli mexanizmlari, xususiyatlarini tahlil qilish va bashorat qilishning samarali vositasiga aylanishi haqida aniq dalillar keltirilgan.
The purpose of this analytical review is to evaluate the current practical application of artificial intelligence to combat terrorism on the Internet, which is considered the greatest threat to security in the Central Asian region. The author shows the modern trends and models and the success of their application in this field as a tool that can process huge amounts of data and detect hidden patterns in them. The review provides clear evidence that artificial intelligence is an effective tool for analyzing and predicting various mechanisms and features of terrorist attacks in the global practice of combating terrorism, including in the Central Asian region.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Adilova F.T. | Laboratoriya mudiri | O'zR Fanlar akademiyasi V.I. Romanovskiy nomidagi Matematika instituti |
| № | Havola nomi |
|---|---|
| 1 | 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 |
| 2 | Kathleen McKendrick International Security Department | August 2019 Artificial Intelligence Prediction and Counterterrorism https://www.chathamhouse.org/sites/default/files/2019-08-07-AICounterterrorism.pdf |
| 3 | 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. |
| 4 | Monaco, L (2017) ‘Preventing the Next Attack; A Strategy for the War on Terrorism’ Foreign Affairs 96(6), pp.23–29. |
| 5 | 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. |
| 6 | 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/ |
| 7 | Elias, B. (2014), Risk-Based Approaches to Airline Passenger Screening, Congressional Research Service Report, 31 March 2014, https://www.hsdl.org/?view&did=752251 |
| 8 | Akhgar, B., Saathoff, G. B., Arabnia, H., Hill, R., Staniforth, A. and Bayerl, P. (2015), Application of Big Data for National Security, Oxford: ButterworthHeinemann. |
| 9 | Van Puyvelde et al. (2017), ‘Beyond the Buzzword: big data and national security decision-making’, p. 1398. |
| 10 | 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 |
| 11 | 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 |
| 12 | 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 |
| 13 | Bickert, M. (2017), ‘Hard Questions: How We Counter Terrorism’, Facebook Newsroom, https://newsroom.fb.com/news/2017/06/how-we-counterterrorism/ |
| 14 | 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/ |
| 15 | Subrahmanian, V. S. (ed.) (2013), Handbook of Computational Approaches to Counterterrorism, New York: Springer |
| 16 | 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. |
| 17 | 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 |
| 18 | Jigsaw (2018), ‘How can technology make people in the world safer?’, https://jigsaw.google.com/vision/ |
| 19 | Jigsaw (2016). ‘The Redirect Method’, https://www.redirectmethod.org |
| 20 | 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 |
| 21 | J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, no. Feb, pp. 281–305, 2012. |
| 22 | 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 |
| 23 | 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. |
| 24 | Meng Xi, Nie Lingyu, Song Jiapeng Big data-based prediction of terrorist attacks Computers& Electrical Engineering,volume 77,July 2019,p.120-127 |
| 25 | 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. |
| 26 | 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. |
| 27 | Firas Saidi, Zouheir Trabelsi A hybrid deep learning-based framework for future terrorist activities modeling and prediction Egyptian Informatics Journal 23 (2022) 437–446 |
| 28 | 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 |
| 29 | W. Guo, K. Gleditsch, and A. Wilson, “Retool AI to forecast and limit wars” Nature, vol. 562, no. 7727, pp. 331–333, 2018. |