Abstract
In this paper, we apply cybersecurity dynamics theory into practical scenarios. We use machine learning models as detection tools of intrusion detection systems and consider cyber attacks against node computers as well as adversarial attacks against machine learning models. We pay our attention to two problems. The first problem is when the network is attacked, how we can observe the states of the network and estimate its equilibrium with a lower cost. We apply an event-based observation and estimation method combined with machine learning-based intrusion detection systems. The second problem is to control the cost and the convergence speed of cybersecurity dynamics when it is under attack. An event-based control method and machine learning-based intrusion detection systems are put into use in this scenario. We simulate both scenarios and analyze the dynamics’ behaviors under an adversarial attack against the machine learning models on intrusion detection systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Bataineh, A., White, G.: Analysis and detection of malicious data exfiltration in web traffic. In: 2012 7th International Conference on Malicious and Unwanted Software, pp. 26–31. IEEE (2012)
Anley, C.: Advanced SQL injection in SQL server applications (2002)
Biggio, B., et al.: Security evaluation of support vector machines in adversarial environments. In: Ma, Y., Guo, G. (eds.) Support Vector Machines Applications, pp. 105–153. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02300-7_4
Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012)
Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. 2, 222–232 (1987)
Giménez, C.T., Villegas, A.P., Marañón, G.Á.: Http data set CSIC 2010. Information Security Institute of CSIC (Spanish Research National Council) (2010)
Gupta, S.: Buffer overflow attack. IOSR J. Comput. Eng. 1(1), 10–23 (2012)
Halfond, W.G., Viegas, J., Orso, A., et al.: A classification of SQL-injection attacks and countermeasures. In: Proceedings of the IEEE International Symposium on Secure Software Engineering, vol. 1, pp. 13–15. IEEE (2006)
Ito, M., Iyatomi, H.: Web application firewall using character-level convolutional neural network. In: 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 103–106. IEEE (2018)
Liu, Y., Corbett, C., Chiang, K., Archibald, R., Mukherjee, B., Ghosal, D.: Detecting sensitive data exfiltration by an insider attack. In: Proceedings of the 4th Annual Workshop on Cyber Security and Information Intelligence Research: Developing Strategies to Meet the Cyber Security and Information Intelligence Challenges Ahead, pp. 1–3 (2008)
Liu, Z., Jia, Z., Lu, W.: Security comparison of machine learning models facing different attack targets. In: Liu, F., Xu, J., Xu, S., Yung, M. (eds.) SciSec 2019. LNCS, vol. 11933, pp. 77–91. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34637-9_6
Liu, Z., Lu, W., Lang, Y.: An event-based parameter switching method for controlling cybersecurity dynamics. arXiv preprint arXiv:2104.13339 (2021)
Liu, Z., Zheng, R., Lu, W., Xu, S.: Using event-based method to estimate cybersecurity equilibrium. IEEE/CAA J. Automatica Sinica 8(2), 455–467 (2020)
Lu, W., Xu, S., Yi, X.: Optimizing active cyber defense. In: Das, S.K., Nita-Rotaru, C., Kantarcioglu, M. (eds.) GameSec 2013. LNCS, vol. 8252, pp. 206–225. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02786-9_13
Mahadev Kumar, V., Kumar, K.: Classification of DDOS attack tools and its handling techniques and strategy at application layer. 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), pp. 1–6 (2016)
Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics (2014)
Raut, U.K.: Log based intrusion detection system. IOSR J. Comput. Eng. 20(5), 15–22 (2018)
Sard, A.: The measure of the critical values of differentiable maps. Bull. Am. Math. Soc. 48(12), 883–890 (1942)
Tekerek, A.: A novel architecture for web-based attack detection using convolutional neural network. Comput. Secur. 100, 102096 (2021)
Torrano-Giménez, C., Perez-Villegas, A., Alvarez Maranón, G.: An anomaly-based approach for intrusion detection in web traffic (2010)
Wang, J., Zhou, Z., Chen, J.: Evaluating CNN and LSTM for web attack detection. In: Proceedings of the 2018 10th International Conference on Machine Learning and Computing, pp. 283–287 (2018)
Xu, S.: Cybersecurity dynamics. In: Proceedings of the 2014 Symposium and Bootcamp on the Science of Security, pp. 1–2 (2014)
Xu, S.: Cybersecurity dynamics: a foundation for the science of cybersecurity. In: Wang, C., Lu, Z. (eds.) Proactive and Dynamic Network Defense, vol. 31, pp. 1–31. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-10597-6_1
Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDOS) flooding attacks. IEEE Commun. Surv. Tutor. 15(4), 2046–2069 (2013)
Zebari, R.R., Zeebaree, S.R., Jacksi, K.: Impact analysis of HTTP and SYN flood DDOS attacks on apache 2 and IIS 10.0 web servers. In: 2018 International Conference on Advanced Science and Engineering (ICOASE), pp. 156–161. IEEE (2018)
Zheng, R., Lu, W., Xu, S.: Preventive and reactive cyber defense dynamics is globally stable. IEEE Trans. Netw. Sci. Eng. 5(2), 156–170 (2017)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Z., Wang, Y., Chen, H., Lu, W. (2021). Simulations of Event-Based Cyber Dynamics via Adversarial Machine Learning. In: Lu, W., Sun, K., Yung, M., Liu, F. (eds) Science of Cyber Security. SciSec 2021. Lecture Notes in Computer Science(), vol 13005. Springer, Cham. https://doi.org/10.1007/978-3-030-89137-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-89137-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89136-7
Online ISBN: 978-3-030-89137-4
eBook Packages: Computer ScienceComputer Science (R0)