COMPLEX METHOD OF DETERMINING THE LOCATION OF SOCIAL NETWORK AGENTS IN THE INTERESTS OF INFORMATION OPERATIONS
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Abstract
The researcher developed a method for determining the location of social network agents in the interest of conducting an information operation based on a comprehensive approach to data analysis of the information system. The relevance of the method is determined by the need to specify the enemy's target audience in the area of the information operation. Results. The author proposed a complex method for determining the location of social network agents, which is based on the combination of data from the analysis of the social connections of the specified agent, geotags and the time of registration of his friends in the social network, databases of IP addresses and geolocations of social network agents. The advantage of the developed method is the possibility of its application without direct access to the devices of agents of the social network that use the data of global positioning satellite systems. Conclusion. The application of the proposed complex method of determining the location of agents of social networks makes it possible to increase the effectiveness of information operations due to a more accurate definition of the enemy's target audience in the area of operations. The direction of improvement of the developed method can be its integration with complex information systems of psychological influence, as well as the use of machine learning methods and algorithms.
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Yevseyev, S., Ponomarenko, V., Laptiev, O. et. al. (2021), Synergy of building cybersecurity systems, RS TECHNOLOGY CENTER, Kharkiv, 188 p., doi: https://doi: 10.15587/978-617-7319-31-2
(2014), AJP-3.10.1. Allied Joint Doctrine for Psychological Operations, Edition B, Version 1, NATO doctrine, September 2014, 106 p., available at: https://info.publicintelligence.net/NATO-PSYOPS.pdf
Bazarnyі, S. V. (2023), “A method of identifying agents of social networks with the greatest influence”, Modern information technologies in the field of security and defense: science, No. 1(46)/2023, pp. 145–150, doi: https://doi.org/10.33099/2311-7249/2023-46-1-145-150.
Mourad, A., Scholer, F., Sanderson, M. and Magdy, W. (2018), “How well did you locate me?: effective evaluation of Twitter user geolocation”, ASONAM '18: Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, August 2018, pp. 437–440, available at: https://dl.acm.org/doi/10.5555/3382225.3382317
Zhong, T., Wang, T., Wang, J., Wu, J. and Zhou, F. (2020), “Multiple-aspect Attentional Graph Neural Networks for Online Social Network User Localization”, IEEE Access (IF3.367), Vol. 8, pp. 95223–95234, doi: https://doi.org/10.1109/ACCESS.2020.2993876
McCallum, A., Nigam, K. (1998), “A Comparison of Event Models for Naive Bayes Text Classification,” AAAI 1998: Learning for Text Categorization, pp. 41–48, available at: http://courses.washington.edu/ling572/papers/mccallum1998_AAAI.pdf
Zhang, W. and Gao, F. (2013), “Performance analysis and improvement of naïve Bayes in text classification application”, Proceedings of the IEEE Conference Anthology, China, pp. 1–4. doi: https://doi.org/10.1109/ANTHOLOGY.2013.6784818
Rahimi, A., Cohn, T. and Baldwin, T. (2017), “A Neural Model for User Geolocation and Lexical Dialectology”, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics Vancouver, Canada, Vol. 2, Short Papers, pp. 209–216, available at: https://aclanthology.org/P17-2033.pdf
Zola, P., Cortez, P. and Carpita, M. (2019), “Twitter user geolocation using web country noun searches,” Decision Support Systems, Vol. 120, pp. 50–59, doi: https://doi.org/10.1016/j.dss.2019.03.006
Semenov, S., Sira, O., Gavrylenko, S. and Kuchuk, N. (2019), “Identification of the state of an object under conditions of fuzzy input data“, Eastern-European Journal of Enterprise Technologies, Vol 1, No 4 (97), pp. 22-30, doi:
https://doi.org/10.15587/1729-4061.2019.157085
Dan, O., Parikh, V. and Davison, B. (2018), “IP Geolocation through Reverse DNS”, Networking and Internet Architecture, abs/1811.04288 (2018), pp. 1–10, doi: https://doi.org/10.48550/arXiv.1811.04288
Kong, L., Liu, Z. and Huang, Y. (2014), “Spot: Locating social media users based on social network context”. Proceedings of the VLDB Endowment, vol. 7, no. 13, pp. 1681–1684, doi: https://doi.org/10.14778/2733004.2733060
Kuchuk, N., Mozhaiev, O., Semenov, S., Haichenko, A., Kuchuk, H., Tiulieniev, S., Mozhaiev, M., Davydov, V., Brusakova, O. and Gnusov, Y. (2023), “Devising a method for balancing the load on a territorially distributed foggy environment”, Eastern-European Journal of Enterprise Technologies, Vol. 1(4-121), pp. 48–55, doi: http://dx.doi.org/10.15587/1729-4061.2023.274177
Sandeep, Singhal (2002), “Top 10 vulnerabilities in Today`s WI-FI Networks”, Computerworld, US, available at: https://www.computerworld.com/article/2577244/top-10-vulnerabilities-in-today-s-wi-fi-networks.html
Podorozhniak, A., Liubchenko, N., Oliinyk, V. and Roh, V. (2023), “Research Application of the Spam Filtering and Spammer Detection Algorithms on Social Media and Messengers”, Advanced Information Systems, Vol. 7, No. 3, pр. 60-66, doi:
https://doi.org/10.20998/2522-9052.2023.3.09
Abdullah, Y. N. (2023), “Accuracy and Coverage Analysis of IP Geolocation Databases”, 2023 International Balkan Conference on Communications and Networking (BalkanCom), İstanbul, Turkiye, pp. 1–6. doi:
https://doi.org/10.1109/BalkanCom58402.2023.10167899
Shmatko, O., Herasymov, S., Lysetskyi, Y., Yevseiev, S., Sievierinov О., Voitko, T., Zakharzhevskyi, A., Makogon, H., Nesterov, A. and Bondarenko, K. (2023), “Development of the automated decision-making system synthesis method in the management of information security channels”, Eastern-European Journal of Enterprise Technologies, Vol. 6/9 (126),
pр. 39–49, doi: https://doi.org/10.15587/1729-4061.2023.293511
Yevseiev, S., Hryshchuk, R., Molodetska, K. et. al. (2022), Modeling of security systems for critical infrastructure facilities, РС ТЕСHNOLOGY СЕNTЕR, Kharkiv, 196 p., doi: https://doi.org/10.15587/978-617-7319-57-2
Kovalenko, A. and Kuchuk, H. (2022), “Methods to Manage Data in Self-healing Systems”, Studies in Systems, Decision and Control, Vol. 425, pp. 113–171, doi: https://doi.org/10.1007/978-3-030-96546-4_3
Dotsenko, N., Chumachenko, I., Galkin, A., Kuchuk, H. and Chumachenko, D. (2023), “Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment”, Sustainability (Switzerland), Vol. 15(19), 14308, doi: https://doi.org/10.3390/su151914308
(2016), “SMS Spam Collection Dataset: Collection of SMS messages tagged as spam or legitimate”, UCI MACHINE LEARNING, available at: https://www.kaggle.com/uciml/sms-spam-collection-dataset
Jensen, G.G., Busch, M.B., Piovesan, M. And Haerter, J.O. (2023), “Nudging cooperation among agents in an experimental social network”, Applied Network Science, Vol. 8(1), 62, doi: https://doi.org/10.1007/s41109-023-00588-x
Fennell, S.C., Gleeson, J.P., Quayle, M., Durrheim, K. and Burke, K. (2023), Agent-based null models for examining experimental social interaction networks, Scientific Reports, 13(1), 5249, doi: https://doi.org/10.1038/s41598-023-32295-z
Dun B., Zakovorotnyi, O. and Kuchuk, N. (2023), “Generating currency exchange rate data based on Quant-Gan model”, Advanced Information Systems, Vol. 7, no. 2, pp. 68–74, doi: http://dx.doi.org/10.20998/2522-9052.2023.2.10
Gomathi, B., Saravana Balaji, B., Krishna Kumar, V., Abouhawwash, M., Aljahdali, S., Masud, M. and Kuchuk, N. (2022), “Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center”, Intelligent Automation and Soft Computing, Vol. 33(3), pp. 1771–1785, doi: http://dx.doi.org/10.32604/iasc.2022.024052
(2023), Python for Beginners. Python Software Foundation, available at: https://www.python.org/about/gettingstarted/
Messaoudi, C., Romdhane, L.B. and Guessoum, Z. (2023), “AMIR: A Multi-agent Approach for Influence Detection in Social Networks”, Lecture Notes in Networks and Systems, 740 LNNS, pp. 242–253, doi: http://dx.doi.org/10.1007/978-3-031-38333-5_25