IDENTIFYING CYBERTHREATS THROUGH SOCIAL MEDIA RESEARCH

The work provides an overview of the main methods of social network analysis used to identify cyber threats. The main types of threats in social networks are indicated and some protection methods for their prevention are described. Typical tasks of social network analysis aimed at detecting cyber threats are, for example, identifying online communities, identifying leaders and experts in communities, analyzing the stability of communities, clustering textual information, etc. With increasing digitization and active use of social networks as a means of communication, the importance of effective monitoring and data analysis to ensure cyber security is becoming more and more relevant. Complexities and challenges associated with processing large amounts of data in social networks are also analyzed, including privacy issues and ethical aspects of user data control. The study provides a holistic view of the use of social media as a tool to proactively identify and prevent cyberthreats, highlighting the importance of integrating analytical systems into the overall cybersecurity framework of organizations and individuals. The study provides a holistic view of the use of social media as a tool for proactive detection and prevention of cyber threats, highlighting the importance of integrating analytical systems into the overall cybersecurity posture of organizations and individuals, while emphasizing the need to improve machine learning and artificial intelligence techniques for deeper and more accurate analysis of user behavior and community dynamics, which can contribute to more effective prevention and neutralization of threats in the digital environment.

1. Carley K. Destabilizing networks. / K. Carley, J. Lee, D. Krackhardt // Connections. – 2002. – Vol. 24, № 3. – R. 79-92. (In English).

2. Dodonov O.G. Infopmatsiini potoki v global'nix komp'yutepnix mepezhax / O.G. Dodonov, D.V. Lande, V.G. Pytyatin. – Kiїv: Naykova dymka, 2009. – 295 c. (In Russian).

3. Stohl C. Networks of Terror: Theoretical Assumptions and Pragmatic Consequences / C. Stohl, M. Stohl // Communication Theory. – 2007. – Vol. 17. – R. 93-124. (In English).

4. Russell M.A. Mining the Social Web: Analyzing Data from Facebook,Twitter, LinkedIn, and Other Social Media Sites / M.A. Russell. – O’Reilly, 2011. – 332 p. (In English).

5. Easley D. Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge University Press / D. Easley, J. Kleinberg. – 2010. – 819 p. (In English).

6. Gpinenko I. Vpliv viptyal'nix cpil'not na infopmatsiiny bezpeky: cychacnii ctan ta tendentsiї pozvitky. / I. Gpinenko, D. Ppokof'єva-Yanchilenko // Ppavove, nopmativne ta metpologichne zabezpechennya cictem zaxicty infopmatsiї v Ykpaїni. – 2012. – № 1(23). – C. 18-23. (In Ukrainian).

7. Coscia M. A classification for community discovery methods in complex networks. / M. Coscia, F. Giannotti, D. Pedreschi // Statistical Analysis and Data Mining. – 2011. – R. 512-546. (In English).

8. Gybanov D. Cotsial'nye ceti: modeli infopmatsionnogo vliyatiya, yppavleniya i ppotivobopctva / D. Gybanov, D. Novikov, A. CHaptishvili. – Mockva, 2010. – 225 c. (In Russian).

9. Byzyn N. Vyyavlenie pepecekayushchixcya coobshchectv v cotsial'nyx cetyax / N. Byzyn, A. Kopshynov. – M.: Inctityt cictemnogo ppogpammipovaniya PAN, 2012. – 18 c. (In Russian).

10. Ykyctov C.C. Podxod k pesheniyu zadachi identifikatsii vliyatel'nyx pazpabotchikov v cotsial'noi ceti gitxab / C.C. Ykyctov, A.G. Kpavets // Izvectiya Volgogpadckogo gocydapctvennogo texnicheckogo univepciteta. – 2012. – № 15(102). – S. 61-66. (In Russian).

11. Liben-Nowell D. The Link Prediction Problem for Social Networks / D. Liben-Nowell, J. Kleinberg // Proceedings of the 12th International Conference on Information and Knowledge Management, N. Y.: ACM Press, 2003. – R. 556-559. (In English).