Abstract
The traditional malicious bot traffic detection technology is usually based on rule matching or statistical analysis, which is not flexible enough and has low detection accuracy. This article systematically analyzes the formation and characteristics of malicious bot traffic. And the WEB log traffic information is extracted, analyzed and selected as feature, finally we use support vector machine algorithm to train the malicious bot traffic detection model and the detection accuracy appears to be quite high. This is a good reference for applying machine learning to the field of cyber security.
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
Similar content being viewed by others
References
Tan, P.N., Kumar, V.: Discovery of web robot sessions based on their navigational patterns. In: Zhong, N., Liu, J. (eds.) Intelligent Technologies for Information Analysis, pp. 193–222. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-662-07952-2_9
Stassopoulou, A., Dikaiakos, M.D.: Web robot detection: a probabilistic reasoning approach. Comput. Netw. 53(3), 265–278 (2009)
Bomhardt, C., Gaul, W., Schmidt-Thieme, L.: Web robot detection-preprocessing web log files for robot detection. In: Bock, H.H., et al. (eds.) New Developments in Classification and Data Analysis, pp. 113–124. Springer, Heidelberg (2005)
Ju, X.: Simulation of web crawler detection algorithm based on hidden Markov model. Comput. Mod. (4), 122–126 (2017)
Stevanovic, D., Vlajic, N., An, A.: Unsupervised clustering of Web sessions to detect malicious and non-malicious website users. Procedia Comput. Sci. 5, 123–131 (2011)
Xia, Z.: Adaptive detection method for abnormal traffic based on self-similarity. Comput. Eng. 36(5), 23–25 (2010)
Thatte, G., Mitra, U., Heidemann, J.: Parametric methods for anomaly detection in aggregate traffic. IEEE/ACM Trans. Netw. 19(2), 512–525 (2011)
Zou, J., Li, H.: Detection of anonymous crawler based on website access behavior. Comput. Technol. Dev. 27(12), 103–107 (2017)
Lei, Y.: Network anomaly traffic detection algorithm based on SVM. In: 2017 International Conference on Robots & Intelligent System (ICRIS), Huai’an, pp. 217–220 (2017)
He, H., Li, N.: An RBF network approach to flatness pattern recognition based on SVM learning. In: 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, pp. 2959–2962 (2006)
Zhao, Z.-D., Lou, Y., Ni, J.-H., Zhang, J.: RBF-SVM and its application on reliability evaluation of electric power system communication network. In: 2009 International Conference on Machine Learning and Cybernetics, Hebei, pp. 1188–1193 (2009)
Deng, Q., Cai, A.: SVM-based loss differentiation mechanism in mobile ad hoc networks. In: 2009 Global Mobile Congress, Shanghai, pp. 1–4 (2009)
Qiu, G., Liao, L., Wu, Z., Du, Q.: Thunderstorm prediction study based on PCA and least square support vector machine. In: 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), XianNing, pp. 2828–2831 (2011)
Acknowledgement
This research is supported by National Natural Science Foundation of China (No. 61772162), National Key R&D Program of China (No. 2018YFB0804102), Zhejiang Key R&D Program of China (No. 2018C01088).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, M., Wu, Z., Lv, H., Wang, J. (2019). A Method of Malicious Bot Traffic Detection. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11983. Springer, Cham. https://doi.org/10.1007/978-3-030-37352-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-37352-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37351-1
Online ISBN: 978-3-030-37352-8
eBook Packages: Computer ScienceComputer Science (R0)