A Survey of Network Requirements for Enabling Effective Cyber Deception (2309.00184v3)
Abstract: In the evolving landscape of cybersecurity, the utilization of cyber deception has gained prominence as a proactive defense strategy against sophisticated attacks. This paper presents a comprehensive survey that investigates the crucial network requirements essential for the successful implementation of effective cyber deception techniques. With a focus on diverse network architectures and topologies, we delve into the intricate relationship between network characteristics and the deployment of deception mechanisms. This survey provides an in-depth analysis of prevailing cyber deception frameworks, highlighting their strengths and limitations in meeting the requirements for optimal efficacy. By synthesizing insights from both theoretical and practical perspectives, we contribute to a comprehensive understanding of the network prerequisites crucial for enabling robust and adaptable cyber deception strategies.
- K. Horák, B. Bošanskỳ, P. Tomášek, C. Kiekintveld, and C. Kamhoua, “Optimizing honeypot strategies against dynamic lateral movement using partially observable stochastic games,” Computers & Security, vol. 87, p. 101579, 2019.
- A. Aydeger, N. Saputro, K. Akkaya, and M. Rahman, “Mitigating crossfire attacks using sdn-based moving target defense,” in 2016 IEEE 41st Conference on Local Computer Networks (LCN), pp. 627–630, IEEE, 2016.
- H. Maleki, S. Valizadeh, W. Koch, A. Bestavros, and M. Van Dijk, “Markov modeling of moving target defense games,” in Proceedings of the 2016 ACM workshop on moving target defense, pp. 81–92, 2016.
- L. Huang and Q. Zhu, “Dynamic bayesian games for adversarial and defensive cyber deception,” in Autonomous cyber deception, pp. 75–97, Springer, 2019.
- M. A. Sayed, A. H. Anwar, C. Kiekintveld, B. Bosansky, and C. Kamhoua, “Cyber deception against zero-day attacks: a game theoretic approach,” in International Conference on Decision and Game Theory for Security, pp. 44–63, Springer, 2022.
- M. A. Sayed, A. H. Anwar, C. Kiekintveld, and C. Kamhoua, “Honeypot allocation for cyber deception in dynamic tactical networks: A game theoretic approach,” arXiv preprint arXiv:2308.11817, 2023.
- M. A. Sayed, M. A. I. Khan, B. A. Allsup, J. Zamora, and P. Aggarwal, “Assessing the influence of different types of probing on adversarial decision-making in a deception game,” arXiv preprint arXiv:2310.10662, 2023.
- W. Han, Z. Zhao, A. Doupé, and G.-J. Ahn, “Honeymix: Toward sdn-based intelligent honeynet,” in Proceedings of the 2016 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization, pp. 1–6, 2016.
- S. Islam, M. A. I. Khan, S. T. Shorno, S. Sarker, and M. A. Siddik, “Performance evaluation of sdn controllers in wireless network,” in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–5, IEEE, 2019.
- M. A. Raju, M. S. Mia, M. A. Sayed, and M. R. Uddin, “Predicting the outcome of english premier league matches using machine learning,” in 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–6, IEEE, 2020.
- S. H. Emon, A. Annur, A. H. Xian, K. M. Sultana, and S. M. Shahriar, “Automatic video summarization from cricket videos using deep learning,” in 2020 23rd International Conference on Computer and Information Technology (ICCIT), pp. 1–6, IEEE, 2020.
- M. Zhu, A. H. Anwar, Z. Wan, J.-H. Cho, C. A. Kamhoua, and M. P. Singh, “A survey of defensive deception: Approaches using game theory and machine learning,” IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2460–2493, 2021.
- M. A. Sayed, M. M. Rahman, M. I. Zaber, and A. A. Ali, “Understanding dhaka city traffic intensity and traffic expansion using gravity model,” in 2017 20th International Conference of Computer and Information Technology (ICCIT), pp. 1–6, IEEE, 2017.
- S. Haque, M. A. Hoque, M. A. I. Khan, and S. Ahmed, “Covid-19 detection using feature extraction and semi-supervised learning from chest x-ray images,” in 2021 IEEE Region 10 Symposium (TENSYMP), pp. 1–5, IEEE, 2021.
- S. H. Emon, M. A. H. Mridha, and M. Shovon, “Automated recognition of rice grain diseases using deep learning,” in 2020 11th International Conference on Electrical and Computer Engineering (ICECE), pp. 230–233, IEEE, 2020.
- S. Mahmud, M. Mohsin, A. Muyeed, S. Nazneen, M. A. Sayed, N. Murshed, T. T. Tonmon, and A. Islam, “Machine learning approaches for predicting suicidal behaviors among university students in bangladesh during the covid-19 pandemic: A cross-sectional study,” Medicine, vol. 102, no. 28, 2023.
- M. Fazle Rabbi, S. Hossain Emon, E. Mahmud Nishat, A. Ferdoushi, C.-C. Huang, M. Fashiar Rahman, et al., “A novel approach for defect detection of wind turbine blade using virtual reality and deep learning,” arXiv e-prints, pp. arXiv–2401, 2023.
- S. Sugrim, S. Venkatesan, J. A. Youzwak, C.-Y. J. Chiang, R. Chadha, M. Albanese, and H. Cam, “Measuring the effectiveness of network deception,” in 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 142–147, IEEE, 2018.
- R. L. Neupane, T. Neely, N. Chettri, M. Vassell, Y. Zhang, P. Calyam, and R. Durairajan, “Dolus: cyber defense using pretense against ddos attacks in cloud platforms,” in Proceedings of the 19th International Conference on Distributed Computing and Networking, pp. 1–10, 2018.
- D. Ma, C. Lei, L. Wang, H. Zhang, Z. Xu, and M. Li, “A self-adaptive hopping approach of moving target defense to thwart scanning attacks,” in International Conference on Information and Communications Security, pp. 39–53, Springer, 2016.
- C.-Y. J. Chiang, Y. M. Gottlieb, S. J. Sugrim, R. Chadha, C. Serban, A. Poylisher, L. M. Marvel, and J. Santos, “Acyds: An adaptive cyber deception system,” in MILCOM 2016-2016 IEEE Military Communications Conference, pp. 800–805, IEEE, 2016.
- I. Anjum, M. Zhu, I. Polinsky, W. Enck, M. K. Reiter, and M. P. Singh, “Role-based deception in enterprise networks,” in Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy, pp. 65–76, 2021.
- A. A. Adebayo and D. B. Rawat, “Cyber deception for wireless network virtualization using stackelberg game theory,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), pp. 1–6, IEEE, 2021.
- S. Achleitner, T. La Porta, P. McDaniel, S. Sugrim, S. V. Krishnamurthy, and R. Chadha, “Cyber deception: Virtual networks to defend insider reconnaissance,” in Proceedings of the 8th ACM CCS international workshop on managing insider security threats, pp. 57–68, 2016.
- N. Sahri and K. Okamura, “Protecting dns services from ip spoofing: Sdn collaborative authentication approach,” in Proceedings of the 11th International Conference on Future Internet Technologies, pp. 83–89, 2016.
- H. Wu, Y. Gu, G. Cheng, and Y. Zhou, “Effectiveness evaluation method for cyber deception based on dynamic bayesian attack graph,” in Proceedings of the 2020 3rd International Conference on Computer Science and Software Engineering, pp. 1–9, 2020.
- D. B. Rawat, N. Sapavath, and M. Song, “Performance evaluation of deception system for deceiving cyber adversaries in adaptive virtualized wireless networks,” in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, pp. 401–406, 2019.