Skip to main content
SpringerLink
Log in

Practical Teaching Reform of Cybersecurity Courses Incorporating Personalized Recommendations

  • Conference paper
  • First Online:
Computer Science and Education. Teaching and Curriculum (ICCSE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2024))

Included in the following conference series:

Abstract

In recent years, with the development of cyber security, the national demand for cybersecurity-related professionals is growing, and the practice of cyber security related practices is increasing, which makes it difficult for students to find suitable practices in the massive practice resources, so the practice recommendations method for students emerges. As an emerging technique, Matrix Completion becomes one of the most powerful techniques to predict missing entries of a low-rank matrix from incomplete samples of its entries. Despite its efficiency in discovering and quantifying the interactions between students and practices, Matrix Completion in the recommender scheme suffers from the problems of low rating density and scalability. To conquer the above challenges as well as to provide quick and high-quality recommendations, we propose partition-based Matrix Completion, a novel recommender scheme that concurrently exploits locality-sensitive hash (LSH) and Matrix Completion. Specifically, taking advantage of the good properties of LSHs, our recommender scheme adopts an LSH hash table to reorder students with similar students buffered in close positions. As a result, the original student-practice rating matrix is partitioned into sub-matrices each containing a group of similar students thus having a lower rank. Matrix Completion is then used in the partitioned matrix to more effectively predict the missing ratings of a student for practice. The experimental results show that our recommendation scheme achieves better recommendation results in terms of practicing recommendation compared to other traditional recommendation methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Knapp, K.J., Maurer, C., Plachkinova, M.: Maintaining a cybersecurity curriculum: professional certifications as valuable guidance. J. Inf. Syst. Educ. 28(2), 101 (2017)

    Google Scholar 

  2. Cabaj, K., Domingos, D., Kotulski, Z., Respício, A.: Cybersecurity education: evolution of the discipline and analysis of master programs. Comput. Secur. 75, 24–35 (2018)

    Article  Google Scholar 

  3. Bobadilla, J., Serradilla, F., Hernando, A., et al.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)

    Article  Google Scholar 

  4. Thorat, P.B., Goudar, R.M., Barve, S.: Survey on collaborative filtering, content-based filtering and hybrid recommendation system. Int. J. Comput. Appl. 110(4), 31–36 (2015)

    Google Scholar 

  5. Wu, L., He, X., Wang, X., Zhang, K., Wang, M.: A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Trans. Knowl. Data Eng. 35(5), 4425–4445 (2022)

    Google Scholar 

  6. Fu, M., Qu, H., Yi, Z., Lu, L., Liu, Y.: A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans. Cybern. 49(3), 1084–1096 (2018)

    Article  Google Scholar 

  7. Burke, R., Felfernig, A., Göker, M.H.: Recommender systems: an overview. AI Mag. 32(3), 13–18 (2011)

    Google Scholar 

  8. Wang, D., Liang, Y., Xu, D., Feng, X., Guan, R.: A content-based recommender system for computer science publications. Knowl.-Based Syst. 157, 1–9 (2018)

    Article  Google Scholar 

  9. Tarus, J.K., Niu, Z., Mustafa, G.: Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif. Intell. Rev. 50, 21–48 (2018)

    Article  Google Scholar 

  10. Rosa, R.L., Schwartz, G.M., Ruggiero, W.V., Rodríguez, D.Z.: A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Trans. Industr. Inf. 15(4), 2124–2135 (2018)

    Article  Google Scholar 

  11. Yang, X., Steck, H., Guo, Y., Liu, Y.: On top-k recommendation using social networks. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 67–74 (2012)

    Google Scholar 

  12. Wang, X., Pan, W., Xu, C.: HGMF: hierarchical group matrix factorization for collaborative recommendation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 769–778 (2014)

    Google Scholar 

  13. Abdi, M.H., Okeyo, G., Mwangi, R.W.: Matrix factorization techniques for context-aware collaborative filtering recommender systems: a survey (2018)

    Google Scholar 

  14. Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Industr. Inf. 10(2), 1273–1284 (2014)

    Article  Google Scholar 

  15. Liu, H., et al.: EDMF: efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Trans. Industr. Inf. 18(7), 4361–4371 (2021)

    Article  MathSciNet  Google Scholar 

  16. Yi, B., et al.: Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Trans. Industr. Inf. 15(8), 4591–4601 (2019)

    Article  Google Scholar 

  17. Jannach, D., Resnick, P., Tuzhilin, A., Zanker, M.: Recommender systems-beyond matrix completion. Commun. ACM 59(11), 94–102 (2016)

    Article  Google Scholar 

  18. Ramlatchan, A., Yang, M., Liu, Q., Li, M., Wang, J., Li, Y.: A survey of matrix completion methods for recommendation systems. Big Data Mining Anal. 1(4), 308–323 (2018)

    Article  Google Scholar 

  19. Chen, X., Lau, N., Jin, R.: Prime: a personalized recommender system for information visualization methods via extended matrix completion. ACM Trans. Interact. Intell. Syst. 11(1), 1–30 (2021)

    Article  Google Scholar 

  20. Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. ACM Comput. Surv. (CSUR) 51(4), 1–36 (2018)

    Article  Google Scholar 

  21. Zhang, M., Chen, Y.: Inductive matrix completion based on graph neural networks, arXiv preprint arXiv:1904.12058 (2019)

  22. Zhong, K., Song, Z., Jain, P., Dhillon, I.S.: Provable non-linear inductive matrix completion. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  23. Ungar, L.H., Foster, D.P.: Clustering methods for collaborative filtering. In: AAAI Workshop on Recommendation Systems, Menlo Park, CA, vol. 1, pp. 114–129 (1998)

    Google Scholar 

  24. González-Manzano, L., de Fuentes, J.M.: Design recommendations for online cybersecurity courses. Comput. Secur. 80, 238–256 (2019)

    Article  Google Scholar 

  25. Natarajan, S., Vairavasundaram, S., Natarajan, S., Gandomi, A.H.: Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data. Expert Syst. Appl. 149, 113248 (2020)

    Article  Google Scholar 

  26. Juan, W., Yue-xin, L., Chun-ying, W.: Survey of recommendation based on collaborative filtering. In: Journal of Physics: Conference Series, vol. 1314, p. 012078. IOP Publishing (2019)

    Google Scholar 

  27. Mehta, R., Rana, K.: A review on matrix factorization techniques in recommender systems. In: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 269–274. IEEE (2017)

    Google Scholar 

  28. Barathy, R., Chitra, P.: Applying matrix factorization in collaborative filtering recommender systems. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 635–639. IEEE (2020)

    Google Scholar 

  29. Guan, X., Li, C.-T., Guan, Y.: Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems. IEEE Access 5, 27668–27678 (2017)

    Article  Google Scholar 

  30. Xu, B., Bu, J., Chen, C., Cai, D.: An exploration of improving collaborative recommender systems via user-item subgroups. In: Proceedings of the 21st International Conference on World Wide Web, pp. 21–30 (2012)

    Google Scholar 

  31. Chi, X., Yan, C., Wang, H., Rafique, W., Qi, L.: Amplified locality-sensitive hashing-based recommender systems with privacy protection. Concurr. Comput. Pract. Exp. 34(14), e5681 (2022)

    Article  Google Scholar 

  32. Hu, H., et al.: Differentially private locality sensitive hashing based federated recommender system. Concurr. Comput. Pract. Exp. 35(14), e6233 (2023)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China [grant 62202156], the Teaching Reform and Research Project of Hunan University of Science and Technology [grant number 2021-76-9 and 2021-76-26], the Hunan Provincial Teaching Research and Reform Project [grant number HNJG-2022-0786 and HNJG-2022-0792], the Hunan Province Degree and Graduate Teaching Reform and Research Project [grant number 2022JGYB130].

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Yuxiang Chen, Xiaofan Zhou, Linshu Chen, Jigang Wen, Ce Yang, Shiwen Zhang & Wei Liang

  2. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Yuxiang Chen

Authors
  1. Yuxiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linshu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jigang Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ce Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shiwen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wei Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Liang .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Wenxing Hong

  2. Xiamen University Malaysia, Sepang, Malaysia

    Geetha Kanaparan

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, Y. et al. (2024). Practical Teaching Reform of Cybersecurity Courses Incorporating Personalized Recommendations. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Teaching and Curriculum. ICCSE 2023. Communications in Computer and Information Science, vol 2024. Springer, Singapore. https://doi.org/10.1007/978-981-97-0791-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0791-1_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0790-4

  • Online ISBN: 978-981-97-0791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation