Reconstructing Trust Models in Professional Cybersecurity: A Game-Theoretic Approach to Insider Threat Prevention in Post-Pandemic Workflows
Keywords:
Trust models, Insider threats, Game theory, Cybersecurity risks, Behavior analysis, Risk detectionAbstract
In this article, the researcher examines the necessity to review the models of building trust in cybersecurity by predominantly focusing on the expansion of insider threats in post-pandemic, remote, and hybrid work environments. This study is based on a game-theoretical modeling of the insider behavior that focuses on the possibility of strategic management and redefinition of trust in organizations in order to avoid any internal threats. Its methodology entails theoretical model-based methodology and analytical case studies that involve the artificial use of data to make realistic situations of insider threats to assess the efficiency of the proposed models. The most important results indicate that the current work structures require updated models of trust, which would provide insider risks detection and prevention to a greater degree with the use of a behavior-based model. This paper will underline the necessity of the development of decentralized cybersecurity activities through the evolution of trust mechanisms and stress the role of game theory in the forecast and governance of insider behaviors. In the end, the findings are very helpful to organizations wishing to enhance their resilience to cyber entrenchments.
References
Al-Ayed, F. (2021). "Zero-Trust Model of Cybersecurity: A Significant Challenge in the Future," 2021 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 852-854, doi: 10.1109/CSCI54926.2021.00200.
Chen, D., Chowdhury, M. M., & Latif, S. (2021). "Data Breaches in Corporate Setting," 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius, Mauritius, 2021, pp. 01-06, doi: 10.1109/ICECCME52200.2021.9590974.
Faisal, M., Ali, I., Khan, M. S., Kim, S. M., & Kim, J. (2020). "Establishment of Trust in Internet of Things by Integrating Trusted Platform Module: To Counter Cybersecurity Challenges," Complexity, 2020, 1–9, https://doi.org/10.1155/2020/6612919.
Fahim, M., & Sillitti, A. (2019). "Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review," in IEEE Access, vol. 7, pp. 81664-81681, doi: 10.1109/ACCESS.2019.2921912.
Hanna, I. (2022). "The Snowden Files," Lau.edu.lb, http://hdl.handle.net/10725/13432.
Lang, M., & Connolly, L. (2021). "Managing the Cybersecurity Risks of Teleworking in the Post-Pandemic 'New Normal,'" SSRN Electronic Journal, https://doi.org/10.2139/ssrn.4146506.
Merrick, K., Hardhienata, M., Shafi, K., & Hu, J. (2016). "A Survey of Game Theoretic Approaches to Modelling Decision-Making in Information Warfare Scenarios," Future Internet, 8(3), 34, https://doi.org/10.3390/fi8030034.
Prabhu, S., & Thompson, N. (2020). "A Unified Classification Model of Insider Threats to Information Security," ACIS 2020 Proceedings, https://aisel.aisnet.org/acis2020/40/.
Putz, B., & Pernul, G. (2019). "Trust Factors and Insider Threats in Permissioned Distributed Ledgers," Lecture Notes in Computer Science, 25–50, https://doi.org/10.1007/978-3-662-60531-8_2.
Rass, S., König, S., & Schauer, S. (2017). "Defending Against Advanced Persistent Threats Using Game-Theory," PLOS ONE, 12(1), e0168675, https://doi.org/10.1371/journal.pone.0168675.
Reveraert, M., & Sauer, T. (2020). "Redefining Insider Threats: A Distinction Between Insider Hazards and Insider Threats," Security Journal, https://doi.org/10.1057/s41284-020-00259-x.
Saxena, N., Hayes, E., Bertino, E., Ojo, P., Choo, K.-K. R., & Burnap, P. (2020). "Impact and Key Challenges of Insider Threats on Organizations and Critical Businesses," Electronics, 9(9), 1460, https://doi.org/10.3390/electronics9091460.
Wang, Y., Wang, Y., Liu, J., Huang, Z., & Xie, P. (2016). "A Survey of Game Theoretic Methods for Cyber Security," 2016 IEEE First International Conference on Data Science in Cyberspace (DSC), Changsha, China, 2016, pp. 631-636, doi: 10.1109/DSC.2016.90.
Muniyandi, V. (2022). Harnessing Roslyn for advanced code analysis and optimization in cloud-based .NET applications on Microsoft Azure. International Journal of Communication Networks and Security, 14(4), 979-990.
Muniyandi, V. (2021). Extending Roslyn for custom code analysis and refactoring in large enterprise applications. International Journal of Science and Technology Research Archive, 3, 271-283.
Chellu, R. (2021). Secure Containerized Microservices Using PKI-Based Mutual TLS in Google Kubernetes Engine.
Chellu, R. (2022). Spectral Analysis of Cryptographic Hash Functions Using Fourier Techniques. Journal of Computational Analysis and Applications, 30(2).
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