Properties of Malicious Social Bots

Cover Page

Cite item

Full Text

Abstract

The paper considers the ability to describe malicious bots using their characteristics, which can be the basis for building models for recognising bot parameters and qualitatively analysing attack characteristics in social networks. The following metrics are proposed using the characteristics of VKontakte social network bots as an example: trust, survivability, price, seller type, speed, and expert quality. To extract these metrics, an approach is proposed that is based on the methods of test purchases and the Turing test. The main advantage of this approach is that it proposes to extract features from the data obtained experimentally, thereby obtaining a more reasonable estimation than the expert approach. Also, an experiment on extracting metrics from malicious bots of the VKontakte social network using the proposed approach is described, and an analysis of the metrics' dependence is carried out. The experiment demonstrates the possibility of metrics extracting and analysis. In general, the proposed metrics and the approach to their extraction can become the basis for the transition from binary attack detection in social networks to a qualitative description of the attacker and his capabilities, as well as an analysis of the evolution of bots.

About the authors

M. V. Kolomeets

Saint-Petersburg Federal Research Center of the Russian Academy of Sciences

Email: kolomeec@comsec.spb.ru
ORCID iD: 0000-0002-7873-2733

A. A. Chechulin

Saint-Petersburg Federal Research Center of the Russian Academy of Sciences; The Bonch-Bruevich Saint-Petersburg State University of Telecommunications

Email: chechulin.aa@sut.ru
ORCID iD: 0000-0001-7056-6972

References

  1. Cresci S. A decade of social bot detection // Communications of the ACM. 2020. Vol. 63. Iss. 10. PP. 72–83. doi: 10.1145/3409116
  2. Ferrara E., Varol O., Davis C., Menczer F., Flammini A. The rise of social bots // Communications of the ACM. 2016. Vol. 59. Iss. 7. PP. 96–104. doi: 10.1145/2818717
  3. Yang C., Harkreader R., Gu, G. Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers // IEEE Transactions on Information Forensics and Security. 2013. Vol. 8. Iss. 8. PP. 1280–1293. doi: 10.1109/TIFS.2013.2267732
  4. Vitkova L. Kolomeec M., Chechulin A. Taxonomy and Bot Threats in Social Networks // Proceedings of the International Russian Automation Conference (RusAutoCon, Sochi, Russia, 04‒10 September 2022). IEEE, 2022. PP. 814‒819. doi: 10.1109/RusAutoCon54946.2022.9896268
  5. Orabi M., Mouheb D., Al Aghbari Z., Kamel I. Detection of Bots in Social Media: A Systematic Review // Information Processing & Management. 2020. Vol 57. Iss. 4. P. 102250. doi: 10.1016/j.ipm.2020.102250
  6. Varol O., Ferrara E., Davis C., Menczer F., Flammini A. Online Human-Bot Interactions: Detection, Estimation, and Characterization // Proceedings of the 11th International AAAI Conference on Web and Social Media. 2017. Vol. 11. Iss. 1. PP. 280‒289. doi: 10.1609/icwsm.v11i1.14871
  7. Stieglitz S., Brachten F., Berthel ́e D., Schlaus M., Venetopoulou C., Veutgen D. Do Social Bots (Still) Act Different to Humans? – Comparing Metrics of Social Bots with those of Humans // Proceedings of the 9th International Conference on Social Computing and Social Media. Human Behavior (SCSM 2017, Vancouver, Canada, 9‒14 July 2017). Lecture Notes in Computer Science. Vol. 10282. Cham: Springer, 2017. PP. 379–395. doi: 10.1007/978-3-319-58559-8_30
  8. Kolomeets M., Chechulin A. Analysis of the Malicious Bots Market // Proceedings of the 29th Conference of Open Innovations Association (FRUCT, Tampere, Finland, 12‒14 May 2021). IEEE, 2021. PP. 199–205. doi: 10.23919/FRUCT52173.2021.9435421
  9. Perdana R.S., Muliawati T.H., Alexandro R. Bot spammer detection in twitter using tweet similarity and time interval entropy // Jurnal Ilmu Komputer dan Informasi. 2015. Vol. 8. Iss. 1. PP. 19–25. doi: 10.21609/jiki.v8i1.280
  10. The Black Market for Social Media Manipulation. Riga: NATO StratCom COE, 2018.
  11. Chavoshi N., Hamooni H., Mueen A. DeBot: Twitter Bot Detection via Warped Correlation // Proceedings of the 16th International Conference on Data Mining (ICDM, Barcelona, Spain, 12‒15 December 2016). IEEE, 2016. PP. 817–822. doi: 10.1109/ICDM.2016.0096
  12. Dorri A., Abadi M., Dadfarnia M. SocialBotHunter: Botnet Detection in Twitter-Like Social Networking Services Using Semi-Supervised Collective Classification // Proceedings of the 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech, Athens, Greece, 12‒15 August 2018). IEEE, 2018. PP. 496–503. doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00097
  13. Vitkova L., Kotenko I., Kolomeets M., Tushkanova O., Chechulin A. Hybrid Approach for Bots Detection in Social Networks Based on Topological, Textual and Statistical Features // Proceedings of the Fourth International Scientific Conference Intelligent Information Technologies for Industry (IITI’19, Ostrava – Prague, Czech Republic, 2–7 December 2019). Advances in Intelligent Systems and Computing. Vol. 1156. Cham: Springer, 2020. PP. 412‒421. doi: 10.1007/978-3-030-50097-9_42
  14. García-Orosa B., Gamallo P., Martín-Rodilla P., Martínez-Castaño R. Hybrid Intelligence Strategies for Identifying, Classifying and Analyzing Political Bots // Social Sciences. 2021. Vol. 10. Iss. 10. P. 357. doi: 10.3390/socsci10100357
  15. Yang K.C., Hui P.M., Menczer F. Bot Electioneering Volume: Visualizing Social Bot Activity During Elections // Companion Proceedings of The 2019 World Wide Web Conference (WWW '19, San Francisco, USA, 13‒17 May 2019). New York: Association for Computing Machinery, 2019. PP. 214–217. doi: 10.1145/3308560.3316499
  16. Adrian R., Kaiser J. The False positive problem of automatic bot detection in social science research // PLoS ONE. 2020. Vol. 15. Iss. 10. P. e0241045. doi: 10.1371/journal.pone.0241045
  17. Boneh D., Grotto A.J., McDaniel P., Papernot N. How Relevant is the Turing Test in the Age of Sophisbots? // IEEE Security & Privacy. 2019. Vol. 17. Iss. 6. PP. 64‒71. doi: 10.1109/MSEC.2019.2934193
  18. Aiello L.M., Barrat A., Schifanella R., Cattuto C., Markines B., Menczer F. Friendship prediction and homophily in social media // ACM Transactions on the Web. 2012. Vol. 6. Iss. 2. PP. 1‒33. doi: 10.1145/2180861.2180866
  19. Kolomeets M. Security Datasets – MKVK2021. URL: https://github.com/guardeec/datasets#mkvk2021 (дата обращения 28.02.2023)
  20. Branitskiy A., Levshun D., Krasilnikova N., Doynikova E., Kotenko I., Tishkov A., Vanchakova N., Chechulin A. Determination of Young Generation’s Sensitivity to the Destructive Stimuli based on the Information in Social Networks // Journal of Internet Services and Information Security. 2019. Vol. 9. Iss. 3. PP. 1‒20.
  21. Проноза А.А., Виткова Л.А., Чечулин А.А., Котенко И.В., Сахаров Д.В. Методика выявления каналов распространения информации в социальных сетях // Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. 2018. Т.14. № 4. С. 362‒377. doi: 10.21638/11702/spbu10.2018.409.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies