Cybersecurity Data Science (CSDS) Corpus

OVERVIEW

For those interested in the rapidly emerging field of cybersecurity data science (CSDS), below is a corpus of 33 book-length works. The list covers publications going back to 2001, although two-thirds of the works (22 out of 33) were published in the last five years (2016 to 2020).

The works cover a broad range of topics, but have a core focus on data-oriented approaches for assuring cybersecurity. Both statistical and machine learning-based approaches are covered.

TOPICAL SUMMARY

In terms of the main topics raised across the corpus, I reviewed each work to produce the following topic list*. The coverage of each topic across the corpus is denoted (% coverage across 33 works):

• Focused use cases (94%)
• Risk quantification (42%)
• Decision support (70%)
• Data management (45%)
• Data collection (82%)
• Scientific methods (24%)
• Feature engineering (85%)
• Statistical methods (94%)
• Anomaly detection (94%)
• Machine learning (82%)
• Model management (82%)
• Visualization (76%)
• Adversarial methods (85%)
• Organizational management (21%)

* The topic list was validated against prior CSDS taxonomy research (Grahn et al., 2017; Bechor & Jung, 2019)

CSDS CORPUS

Here is a reverse chronological listing of 33 cybersecurity data science (CSDS) book-length works. This is my current understanding of a working corpus for the emerging CSDS field. If you feel there are important book-length works missing, please let me know (scott @ sark7.com). A short synopsis of the works follows and ends with a reference listing.

1. Cybersecurity Analytics (Verma & Marchette, 2020)
2. Data Science in Cybersecurity and Cyberthreat Intelligence (Sikos & Choo (Eds.), 2020)
3. Deep Learning Applications for Cyber Security (Alazab & Tang (Eds.), 2019)
4. Machine Learning for Computer and Cyber Security (Gupta & Sheng (Eds.), 2019)
5. Hands-On Artificial Intelligence for Cybersecurity (Parisi, 2019)
6. Machine Learning for Cybersecurity Cookbook (Tsukerman, 2019)
7. Hands-On Machine Learning for Cybersecurity (Halder & Ozdemir, 2019)
8. Mastering Machine Learning for Penetration Testing (Chebbi, 2018)
9. Malware Data Science: Attack Detection and Attribution (Saxe & Sanders, 2018)
10. AI in Cybersecurity (Sikos (Ed.), 2018)
11. Guide to Vulnerability Analysis for Computer Networks and Systems (Parkinson et al. (Eds.), 2018)
12. Data Science for Cybersecurity (Heard et al. (Eds.), 2018)
13. Machine Learning & Security (Chio & Freeman, 2018)
14. Big Data Analytics in Cybersecurity (Savas & Deng (Eds.), 2017)
15. Research Methods for Cyber Security (Edgar & Manz, 2017)
16. Information Fusion for Cyber-Security Analytics (Alsmadi et al. (Eds.), 2017)
17. Introduction to Machine Learning with Applications in Information Security (Stamp, 2017)
18. Data Analytics and Decision Support for Cybersecurity (Carrascosa et al. (Eds.), 2017)
19. How to Measure Anything in Cybersecurity Risk (Hubbard & Seiersen, 2016)
20. Cybersecurity and Applied Mathematics (Metcalf & Casey, 2016)
21. Dynamic Networks and Cyber-Security (Adams & Heard (Eds.), 2016)
22. Essential Cybersecurity Science (Dykstra, 2016)
23. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques (Baesens et al., 2015)
24. Data-Driven Security (Jacobs & Rudis, 2014)
25. Data Analysis for Network Cyber-Security (Adams & Heard (Eds.), 2014)
26. Network Security Through Data Analysis (Collins, 2014)
27. Applied Network Security Monitoring (Sanders & Smith, 2013)
28. Network Anomaly Detection: A Machine Learning Perspective (Bhattacharyya & Kalita, 2013)
29. Data Mining and Machine Learning in Cybersecurity (Dua & Du, 2011)
30. Intrusion Detection: A Machine Learning Approach (Yu & Tsai, 2011)
31. Machine Learning and Data Mining for Computer Security (Maloof (Ed.), 2006)
32. Statistical Methods in Computer Security (Chen (Ed.), 2005)
33. Computer Intrusion Detection and Network Monitoring (Marchette, 2001)

COMMENTARY

It is notable that risk quantification (42%), data management (45%), scientific methods (24%), and organizational management (21%) are less-well-covered across the corpus. My recent research interviewing 50 global CSDS practitioners suggests that these topics are of central importance to the success of CSDS implementations.

The importance of addressing gaps concerning organizational and methodological perspectives is highlighted by practitioner observations that organizational factors are a key shortcoming and challenge in security implementations (Ponemon Institute, 2017).

SHORT SYNOPSES

(pending)

REFERENCES

• Adams, N., & Heard, N. (2016). Dynamic Networks and Cyber-Security (N. Adams & N. Heard Eds.). London: World Scientific Publishing Ltd.
• Adams, N., & Heard, N. (Eds.). (2014). Data Analysis for Network Cyber-Security. London, UK: Imperial College Press.
• Alazab, M., & Tang, M. (Eds.). (2019). Deep Learning Applications for Cyber Security. Switzerland: Springer.
• Alsmadi, I. M., Karabatis, G., & AlEroud, A. (Eds.). (2017). Information Fusion for Cyber-Security Analytics. Switzerland: Springer.
• Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: Wiley.
• Bechor, T., & Jung, B. (2019). Current State and Modeling of Research Topics in Cybersecurity and Data Science. Systemics, Cybernetics and Informatics, 17(1), 27.
• Bhattacharyya, D. K., & Kalita, J. K. (2013). Network Anomaly Detection: A Machine Learning Perspective(Kindle Edition ed.).
• Carrascosa, I. P., Kalutarage, H. K., & Huang, Y. (Eds.). (2017). Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. Switzerland: Springer.
• Chebbi, C. (2018). Mastering Machine Learning for Penetration Testing. Birmingham, UK: Packt.
• Chen, W. W. S. (Ed.) (2005). Statistical Methods in Computer Security. New York, U.S.: Marcel Dekker.
• Chio, C., & Freeman, D. (2018). Machine Learning & Security. California, U.S.: O’Reilly.
• Collins, M. (2014). Network Security Through Data Analysis. California, U.S.: O’Reilly.
• Dua, S., & Du, X. Data Mining and Machine Learning in Cybersecurity. London: CRC Press.
• Dua, S., & Du, X. (2011). Data Mining and Machine Learning in Cybersecurity. London: CRC Press.
• Dykstra, J. (2016). Essential Cybersecurity Science. Sebastopol, CA, U.S.A.: O’Reilly Media, Inc.
• Edgar, T. W., & Manz, D. O. (2017). Research Methods for Cyber Security. Kindle Edition: Elsevier.
• Grahn, K., Westerlund, M., & Pulkkis, G. (2017). Analytics for Network Security: A Survey and Taxonomy. In I. M. Alsmadi, G. Karabatis, & A. AlEroud (Eds.), Information Fusion for Cyber-security Analytics.
• Gupta, B. B., & Sheng, M. (Eds.). (2019). Machine Learning for Computer and Cyber Security: Principles, Algorithms, and Practices. Florida, U.S.: Taylor & Francis.
• Halder, S., & Ozdemir, S. (2018). Hands-On Machine Learning for Cybersecurity. Birmingham, UK: Packt.
• Heard, N., Adams, N., Rubin-Delanchy, P., & Turcotte, M. (Eds.). (2018). Data Science for Cyber-Security. London, UK: World Scientific.
• Hubbard, D. W., & Seiersen, R. (2016). How to Measure Anything in Cybersecurity Risk. New Jersey, U.S.: Wiley.
• Jacobs, J., & Rudis, B. (2014). Data-Driven Security: Analysis, Visualization and Dashboards.
• Maloof, M. A. (Ed.) (2006). Machine Learning and Data Mining for Computer Security: Methods and Applications. London, UK: Springer.
• Marchette, D. J. (2001). Computer Intrusion Detection and Network Monitoring. New York, U.S.: Springer.
• Metcalf, L., & Casey, W. (Eds.). (2016). Cybersecurity and Applied Mathematics. London, UK: Imperial College Press.
• Parisi, A. (2019). Hands-On Artificial Intelligence for Cybersecurity. Birmingham, UK: Packt.
• Parkinson, S., Crampton, A., & Hill, R. (Eds.). (2018). Guide to Vulnerability Analysis for Computer Networks and Systems: An Artificial Intelligence Approach. Switzerland: Springer.
• Sanders, C., & Smith, J. (2013). Applied Network Security Monitoring: Collection, Detection, and Analysis (D. J. Bianco Ed.). Amsterdam, Netherlands: Elsevier.
• Savas, O., & Deng, J. (Eds.). (2017). Big Data Analytics in Cybersecurity. Boca Raton, Florida, U.S.: CRC Press.
• Saxe, J., & Sanders, H. (2018). Malware Data Science: Attack Detection and Attribution. San Francisco, U.S.: No Starch Press, Inc.
• Sikos, L. F. (Ed.) (2018). AI in Cybersecurity (Vol. 151). Switzerland: Springer.
• Sikos, L. F., & Choo, K.-K. R. (Eds.). (2020). Data Science in Cybersecurity and Cyberthreat Intelligence. Switzerland: Springer.
• Stamp, M. (2017). Introduction to Machine Learning with Applications in Information Security. London, UK: CRC Press.
• Tsukerman, E. (2019). Machine Learning for Cybersecurity Cookbook. Birmingham, UK: Packt.
• Verma, R. M., & Marchette, D. (2020). Cybersecurity Analytics. Boca Raton, FL, U.S.: CRC Press.
• Yu, Z., & Tsai, J. J. P. (2011). Instrusion Detection: A Machine Learning Approach (Vol. 3). London, UK: Imperial College Press.

© COPYRIGHT 2020 Scott Mongeau

This is an excerpt from a forthcoming research manuscript undertaken at Nyenrode Business University