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Malicious Human Behaviour in Information System Security: Contribution to a Threat Model for Event Detection Algorithms

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Foundations and Practice of Security (FPS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13877))

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Abstract

Among the issues the information system security community has to fix, the security of both data and algorithms is a concern. The security of algorithms is dependent on the reliability of the input data. This reliability is questioned, especially when the data is generated by humans (or bots operated by humans), such as in online social networks. Event detection algorithms are an example of technology using this type of data, but the question of the security is not systematically considered in this literature. We propose in this paper a first contribution to a threat model to overcome this problem. This threat model is composed of a description of the subject we are modelling, assumptions made, potential threats and defence strategies. This threat model includes an attack classification and defensive strategies which can be useful for anyone who wants to create a resilient event detection algorithm using online social networks.

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References

  1. COSMIN Taxonomy of Measurement Properties \(\bullet \) COSMIN. https://www.cosmin.nl/tools/cosmin-taxonomy-measurement-properties/

  2. Abkenar, S.B., Kashani, M.H., Akbari, M., Mahdipour, E.: Twitter Spam Detection: A Systematic Review. arXiv:2011.14754 [cs] (2020). version: 2

  3. Alsmadi, I., et al.: Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions. arXiv:2110.13980 [cs] (2021). http://arxiv.org/abs/2110.13980

  4. Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retr. 12(4), 461–486 (2009). https://doi.org/10.1007/s10791-008-9066-8

    Article  Google Scholar 

  5. Atefeh, F., Khreich, W.: A Survey of techniques for event detection in Twitter. Comput. Intell. 31(1), 132–164 (2015). https://doi.org/10.1111/coin.12017

    Article  MathSciNet  Google Scholar 

  6. Biggio, B., Fumera, G., Roli, F.: Design of robust classifiers for adversarial environments. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 977–982 (2011). https://doi.org/10.1109/ICSMC.2011.6083796, ISSN: 1062-922X

  7. Brückner, M., Kanzow, C., Scheffer, T.: Static prediction games for adversarial learning problems. J. Mach. Lear. Res. 13(1), 2617–2654 (2012)

    MathSciNet  MATH  Google Scholar 

  8. de Casanove, O., Sèdes, F.: Apprentissage adverse et algorithmes de détection d’évènements : une première typologie. In: Rendez-vous de la Recherche et de l’Enseignement de la Sécurité des Systèmes d’Information (RESSI 2022) (2022). https://hal.archives-ouvertes.fr/hal-03668829, poster

  9. Chan, P.P.K., Yang, C., Yeung, D.S., Ng, W.W.Y.: Spam filtering for short messages in adversarial environment. Neurocomputing 155, 167–176 (2015). https://doi.org/10.1016/j.neucom.2014.12.034

    Article  Google Scholar 

  10. Duddu, V.: A survey of adversarial machine learning in cyber warfare. Def. Sci. J. 68(4), 356 (2018)

    Article  Google Scholar 

  11. Hasan, M., Orgun, M.A., Schwitter, R.: A survey on real-time event detection from the Twitter data stream. J. Inf. Sci. 44(4), 443–463 (2018). https://doi.org/10.1177/0165551517698564

    Article  Google Scholar 

  12. Hasan, M., Orgun, M.A., Schwitter, R.: Real-time event detection from the Twitter data stream using the TwitterNews+ Framework. Inf. Process. Manage. 56(3), 1146–1165 (2019). https://doi.org/10.1016/j.ipm.2018.03.001

    Article  Google Scholar 

  13. Imam, N.H., Vassilakis, V.G.: A survey of attacks against Twitter spam detectors in an adversarial environment. Robotics 8(3), 50 (2019). https://doi.org/10.3390/robotics8030050

    Article  Google Scholar 

  14. Khandpur, R.P., Ji, T., Jan, S., Wang, G., Lu, C.T., Ramakrishnan, N.: Crowdsourcing cybersecurity: cyber attack detection using social media. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1049–1057 (2017)

    Google Scholar 

  15. Kumar, S., Liu, H., Mehta, S., Subramaniam, L.V.: From Tweets to Events: Exploring a Scalable Solution for Twitter Streams. arXiv:1405.1392 [cs] (2014)

  16. Mazoyer, B., Cagé, J., Hervé, N., Hudelot, C.: A French corpus for event detection on Twitter. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 6220–6227. European Language Resources Association, Marseille, France (2020)

    Google Scholar 

  17. Mazurczyk, W., Drobniak, S., Moore, S.: Towards a systematic view on cybersecurity ecology. In: Akhgar, B., Brewster, B. (eds.) Combatting Cybercrime and Cyberterrorism. ASTSA, pp. 17–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-38930-1_2

    Chapter  Google Scholar 

  18. McMinn, A.J., Jose, J.M.: Real-time entity-based event detection for Twitter. In: Mothe, J., et al. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 65–77. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24027-5_6

    Chapter  Google Scholar 

  19. OWASP: Threat modeling (2022). https://owasp.org/www-community/Threat_Modeling

  20. Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to Twitter. In: Human Language Technologies: The 2010 Annual Conference of the north American Chapter of the Association For Computational Linguistics, pp. 181–189 (2010)

    Google Scholar 

  21. Ritter, A., Wright, E., Casey, W., Mitchell, T.: Weakly supervised extraction of computer security events from Twitter. In: Proceedings of the 24th International Conference on World Wide Web, pp. 896–905. WWW 2015, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2015). https://doi.org/10.1145/2736277.2741083

  22. Sabottke, C., Suciu, O., Dumitras, T.: Vulnerability disclosure in the age of social media: exploiting twitter for predicting real-world exploits. In: 24th USENIX Security Symposium (USENIX Security 15), pp. 1041–1056. USENIX Association, Washington, D.C. (2015), https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/sabottke

  23. Samonas, S., Coss, D.: The CIA strikes back: redefining confidentiality, integrity and availability in security. J. Inf. Syst. Sec. 10(3), 1–25 (2014)

    Google Scholar 

  24. Vamvoudakis, K.G., Hespanha, J.P., Sinopoli, B., Mo, Y.: Adversarial detection as a zero-sum game. In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pp. 7133–7138 (2012). https://doi.org/10.1109/CDC.2012.6426383

  25. Wang, X., Li, J., Kuang, X., Tan, Y.A., Li, J.: The security of machine learning in an adversarial setting: a survey. J. Parallel Distrib. Comput. 130, 12–23 (2019). https://doi.org/10.1016/j.jpdc.2019.03.003, https://www.sciencedirect.com/science/article/pii/S0743731518309183

  26. Wu, C., Li, X., Pan, W., Liu, J., Wu, L.: Zero-sum game-based optimal secure control under actuator attacks. IEEE Trans. Autom. Control 66(8), 3773–3780 (2021). https://doi.org/10.1109/TAC.2020.3029342

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhou, R., Lin, J., Liu, L., Ye, M., Wei, S.: Analysis of SDN attack and defense strategy based on zero-sum game. In: Ren, J., et al. (eds.) BICS 2019. LNCS (LNAI), vol. 11691, pp. 479–485. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39431-8_46

    Chapter  Google Scholar 

  28. Zhou, Y., Kantarcioglu, M., Xi, B.: A game theoretic perspective on adversarial machine learning and related cybersecurity applications. In: Game Theory and Machine Learning for Cyber Security, Chapter 13, pp. 231–269. John Wiley & Sons, Ltd (2021). https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119723950.ch13

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Author information

Authors and Affiliations

  1. Université Toulouse III - Paul Sabatier, 118 route de Narbonnes, 31062 CEDEX 9, Toulouse, France

    Olivier de Casanove & Florence Sèdes

  2. Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France

    Olivier de Casanove & Florence Sèdes

Authors
  1. Olivier de Casanove
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  2. Florence Sèdes
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Corresponding author

Correspondence to Olivier de Casanove .

Editor information

Editors and Affiliations

  1. University of Ottawa, Ottawa, ON, Canada

    Guy-Vincent Jourdan

  2. Université Grenoble Alpes, Grenoble, France

    Laurent Mounier

  3. University of Ottawa, Ottawa, ON, Canada

    Carlisle Adams

  4. University Toulouse III Paul Sabatier, Toulouse, France

    Florence Sèdes

  5. Telecom SudParis, Palaiseau, France

    Joaquin Garcia-Alfaro

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Casanove, O.d., Sèdes, F. (2023). Malicious Human Behaviour in Information System Security: Contribution to a Threat Model for Event Detection Algorithms. In: Jourdan, GV., Mounier, L., Adams, C., Sèdes, F., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2022. Lecture Notes in Computer Science, vol 13877. Springer, Cham. https://doi.org/10.1007/978-3-031-30122-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-30122-3_13

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  • Print ISBN: 978-3-031-30121-6

  • Online ISBN: 978-3-031-30122-3

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