Big data security analyses

Research Areas: Network Security

Principal Investigator: Baijian Yang

Study how to better use macine learning to quickly identify events in big data environment.

Representative Publications

  • Zhang, T., Yang, B. (2016). Box-Cox Transformation in Big Data. Technometrics. www.tandfonline.com/loi/utch20

  • S. Nanda, F. Zafari, C. DeCusatisy, E. Wedaaz and B. Yang , “Predicting Network Attack Patterns in SDN using Machine Learning Approach”, IEEE NFV-SDN 2016, Palo Alto, CA, USA

  • T. Zhang and B. Yang, “Big Data Dimension Reduction using PCA”, to appear IEEE SmartCloud 2016, NYC, USA, 2016


  • B. Yang and T. Zhang, “A Scalable Feature Selection and Model Updating Approach for Big Data Machine Learning”, to appear IEEE SmartCloud 2016, NYC, USA, 2016


  • Zhang, T., & Yang (2018), B. Dimension reduction for big data. Statistics and Its Interface, 11(2), 295-306.

  • Ryu, S.-H.G, & Yang, B. Comparison of Machine Learning Algorithms and Their Ensembles for Botnet Detection. Dekalb, IL: International Conference of Information and Computer Technology, 2018.

  • Zhang, T., & Yang, B. (2017). An exact approach to ridge regression for big data. Computational Statistics, 32(3), 909-928.

Keywords: big data, intrusion detection, machine learning

Coming Up!

Our annual security symposium will take place on April 7th and 8th, 2020.
Purdue University, West Lafayette, IN

More Information