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!
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.
Data Mining by 
Network Security Through Data Analysis by I have also added a direct link to the O'Reilly Learning database as a backup. Sign in with your Florida Tech email address.
Frank Eibe, MA Hall, and IH Witten. The WEKA workbench.
online appendix for "Data Mining: Practical machine learning tools
and techniques". Morgan Kaufmann, 2016.
https://www.cs.waikato.ac.nz/ml/weka/book.html
Hansman, S., & Hunt, R. (2005) A taxonomy of network and computer attacks. Computers & Security, 24(1):31–43.
https://www-sciencedirect-com.portal.lib.fit.edu/science/article/pii/S0167404804001804
Textbook
Data Mining Chapter 1
Erhard Rahm and Hong Hai Do. Data cleaning: Problems and
current approaches. IEEE Data Eng. Bull., 23(4):3{13, 2000.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.8661
Textbooks
Data Mining Chapters 2 & 8
Network Security Chapter 1
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.
https://ieeexplore-ieee-org.portal.lib.fit.edu/document/8386762
Textbook
Data Mining Chapters 4 & 6
Textbook
Data Mining Chapter 5 and Appendix B.5
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.
https://link-springer-com.portal.lib.fit.edu/book/10.1007%2F978-3-319-65188-0
Textbook
Data Mining Chapter 7 and 9.1-9.3
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.
https://www-sciencedirect-com.portal.lib.fit.edu/science/article/pii/S1389128608003927
Textbook
Data Mining Chapter 12
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.
https://ieeexplore-ieee-org.portal.lib.fit.edu/document/6108143
R. Mendes and J. P. Vilela. Privacy-preserving data mining: Meth-
ods, metrics, and applications. IEEE Access, 5:10562{10582, 2017.
https://ieeexplore-ieee-org.portal.lib.fit.edu/document/7950921
Faheem Ullah and M. Ali Babar. Architectural tactics for big data
cybersecurity analytic systems: A review, 2018.
https://arxiv.org/abs/1802.03178
Textbook
Data Mining Chapter 13.1-13.3