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DeapSECURE: Empowering Students for Data- and Compute-Intensive Research in Cybersecurity through Training

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Published:28 July 2019Publication History

ABSTRACT

As the volume and sophistication of cyber-attacks grow, cybersecurity researchers, engineers and practitioners rely on advanced cyberinfrastructure (CI) techniques like big data and machine learning, as well as advanced CI platforms, e.g., cloud and high-performance computing (HPC) to assess cyber risks, identify and mitigate threats, and achieve defense in depth. There is a training gap where current cybersecurity curricula at many universities do not introduce advanced CI techniques to future cybersecurity workforce. At Old Dominion University (ODU), we are bridging this gap through an innovative training program named DeapSECURE (Data-Enabled Advanced Training Program for Cyber Security Research and Education). We developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Each workshop includes a lecture providing the motivation and context for a CI technique, which is then examined during a hands-on session. The modules are delivered through (1) monthly workshops for ODU students, and (2) summer institutes for students from other universities and Research Experiences for Undergraduates participants. Future plan for the training program includes an online continuous learning community as an extension to the workshops, and all learning materials available as open educational resources, which will facilitate widespread adoption, adaptations, and contributions. The project leverages existing partnerships to ensure broad participation and adoption of advanced CI techniques in the cybersecurity community. We employ a rigorous evaluation plan rooted in diverse metrics of success to improve the curriculum and demonstrate its effectiveness.

References

  1. National Initiative for Cybersecurity Education. 2019. Cyberseek heatmap. http://cyberseek.org/heatmap.html. Retrieved April 5, 2019.Google ScholarGoogle Scholar
  2. Jason R. Hamlet, Curtis M. Keliiaa. 2010. Assessment of current cybersecurity practices in the public domain: cyber indications and warnings domain. Sandia National Laboratory Technical Report SAND2010-4765.Google ScholarGoogle Scholar
  3. Jason R. Hamlet, Curtis M. Keliiaa. 2010. National cyber defense high performance computing and analysis: concepts, planning and roadmap. Sandia National Laboratory Technical Report SAND2010-4766.Google ScholarGoogle Scholar
  4. Qiao Zhang, Cong Wang, Hongyi Wu, Chunsheng Xin and Tran V. Phuong. 2018, GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 3933--3939 (2018). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Greg Wilson. 2006. Software Carpentry: Getting Scientists to Write Better Code by Making Them More Productive. Computing in Science & Engineering 8(6), 66--69 (Nov-Dec 2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Software Carpentry. 2019. https://software-carpentry.org/. Retrieved April 8, 2019.Google ScholarGoogle Scholar
  7. Pittsburgh Supercomputing Center. 2019. XSEDE HPC Workshop Series.https://psc.edu/xsede-hpc-series-all-workshops. Retrieved April 8, 2019.Google ScholarGoogle Scholar
  8. John Urbanic and Thomas Maiden. 2018. Evaluating the Wide Area Classroom After 10,500 HPC Students. Presented at EduHPC-18: Workshop on Education for High-Performance Computing (Dallas, TX). https://grid.cs.gsu.edu/~tcpp/curriculum/sites/default/files/TohnUrbanic_1.pdf.Google ScholarGoogle ScholarCross RefCross Ref
  9. Yang Lu, Li Da Xu. 2018. Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics. In IEEE Internet of Things Journal (Early Access). Retrieved April 8, 2019.Google ScholarGoogle Scholar
  10. Peng Jiang, Hongyi Wu, Cong Wang, Chunsheng Xin. 2018. Virtual MAC Spoofing Detection through Deep Learning. In Proceedings of 2018 IEEE International Conference on Communications (ICC), 1--6 (2018).Google ScholarGoogle ScholarCross RefCross Ref
  11. Weize Yu, Selçuk Köse. 2017. A Lightweight Masked AES Implementation for Securing IoT Against CPA Attacks. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(11), 2934--2944 (2017).Google ScholarGoogle ScholarCross RefCross Ref
  12. Raghul Gunasekaran, Sarp Oral, David Dillow, Byung Park, Galen Shipman, Al Geist. 2011. Real-Time System Log Monitoring/Analytics Framework. Presented at Cray User Group Conference (Fairbanks, AK). https://www.osti.gov/biblio/1056901 and https://pdfs.semanticscholar.org/9efa/4559c6a3ccf899bb49f668f67e1214c54e0f.pdf.Google ScholarGoogle Scholar
  13. Cloudera, Inc. 2018. FireEye: uncovering zero-day and advanced persistent threats more quickly. https://www.cloudera.com/about/customers/fireeye.html. Retrieved December 25, 2018.Google ScholarGoogle Scholar
  14. Min Du, Feifei Li, Guineng Zheng, Vivek Srikumar. 2017. DeepLog: Anomaly Detection and Diagnosis from System Logs Through Deep Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1285--1298 (2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Faheem Ullah, M. Ali Babar. 2018. Architectural Tactics for Big Data Cybersecurity Analytic Systems: A Review. https://arxiv.org/abs/1802.03178. Retrieved April 8, 2019.Google ScholarGoogle Scholar
  16. Alvaro A. Cárdenas, Pratyusa K. Manadhata, Sreeranga P. Rajan. 2013. Big Data Analytics for Security. IEEE Security & Privacy, 11(6), 74--76 (Nov-Dec 2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. The Carpentries. 2018. Styles for the Carpentries lessons. https://github.com/carpentries/styles. Retrieved October 10, 2018.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Other conferences
      PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)
      July 2019
      775 pages
      ISBN:9781450372275
      DOI:10.1145/3332186
      • General Chair:
      • Tom Furlani

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      New York, NY, United States

      Publication History

      • Published: 28 July 2019

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