Yao, ChengchaoLuo, XiaoZincir-Heywood, A. Nur2019-02-282019-02-282017-11Yao, C., Luo, X., & Zincir-Heywood, A. N. (2017). Data analytics for modeling and visualizing attack behaviors: A case study on SSH brute force attacks. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–8). https://doi.org/10.1109/SSCI.2017.8280913https://hdl.handle.net/1805/18511In this research, we explore a data analytics based approach for modeling and visualizing attack behaviors. To this end, we employ Self-Organizing Map and Association Rule Mining algorithms to analyze and interpret the behaviors of SSH brute force attacks and SSH normal traffic as a case study. The experimental results based on four different data sets show that the patterns extracted and interpreted from the SSH brute force attack data sets are similar to each other but significantly different from those extracted from the SSH normal traffic data sets. The analysis of the attack traffic provides insight into behavior modeling for brute force SSH attacks. Furthermore, this sheds light into how data analytics could help in modeling and visualizing attack behaviors in general in terms of data acquisition and feature extraction.enPublisher Policydata visualizationself-organizing feature mapsdata analyticsConference proceedings