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CYB 5675 - Data Mining for Cybersecurity

Getting Started

Welcome to the CYB 5675 -  Data Mining for Cybersecurity research guide! Here you will learn about the library resources available to you and find readings from the Evans Library selected by your instructor. 

Please note that the readings listed on this guide are ones available through Florida Tech's Library or web resource. 

Remember that if you have questions or need assistance with these resources, click on the Ask A Librarian link on this page,  - we are happy to help you! 



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Textbooks for CYB 5675

To access the ebooks below, click on the title and then sign in with your Florida Tech email address.

NOTE:  In the weekly readings, these are referred to as Data Mining and Network Security, respectively.



I have also added a direct link to the O'Reilly Learning database as a backup. Sign in with your Florida Tech email address.

Week 1 Readings

Frank Eibe, MA Hall, and IH Witten. The WEKA workbench.
online appendix for "Data Mining: Practical machine learning tools
and techniques". Morgan Kaufmann, 2016.


Hansman, S., & Hunt, R. (2005) A taxonomy of network and computer attacks. Computers & Security, 24(1):31–43.



Data Mining Chapter 1

Week 2 Readings

Erhard Rahm and Hong Hai Do. Data cleaning: Problems and
current approaches. IEEE Data Eng. Bull., 23(4):3{13, 2000.



Data Mining Chapters 2 & 8

Network Security Chapter 1


Week 3 Readings

P. Mishra, V. Varadharajan, U. Tupakula, and E. S. Pilli. A detailed
investigation and analysis of using machine learning techniques for
intrusion detection. IEEE Communications Surveys Tutorials, pages
1-1, 2018.



Data Mining Chapters 4 & 6

Week 4 Reading


Data Mining Chapter 5 and Appendix B.5

Week 5 Readings

Bhuyan, M. H., Bhattacharyya, D. K., and Kalita, J. K. (2017). Network Traffic Anomaly Detection Techniques and Systems, pages 115–169. Springer International Publishing, Cham. 




Data Mining Chapter 7 and 9.1-9.3

Week 6 Readings

Roberto Perdisci, Davide Ariu, Prahlad Fogla, Giorgio Giacinto,
and Wenke Lee. McPAD:
A multiple classifier system for accurate
payload-based anomaly detection. Computer Networks, 53(6):864-
881, 2009.



Data Mining Chapter 12


Week 7 Readings

Cynthia Dwork. The promise of differential privacy. a tutorial on
algorithmic techniques. In 52nd Annual IEEE Symposium on Foun-
dations of Computer Science, October 2011.


R. Mendes and J. P. Vilela. Privacy-preserving data mining: Meth-
ods, metrics, and applications. IEEE Access, 5:10562{10582, 2017.



Week 8 Reading

Faheem Ullah and M. Ali Babar. Architectural tactics for big data
cybersecurity analytic systems: A review, 2018.




Data Mining Chapter 13.1-13.3