Skip to main content

A Search Engine for Scientific Publications: A Cybersecurity Case Study

  • Conference paper
  • First Online:
Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference (DCAI 2021)

Abstract

Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Suryotrisongko, H., Musashi, Y.: Review of cybersecurity research topics, taxonomy and challenges: Interdisciplinary perspective. In: 2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA), pp. 162–167 (2019)

    Google Scholar 

  2. Lu, Y.: Cybersecurity research: a review of current research topics. J. Ind. Integration Manag. 03, 08 (2018)

    Google Scholar 

  3. Rawung, R.H., Putrada, A.G.: Cyber physical system: paper survey. In: 2014 International Conference on ICT For Smart Society (ICISS), pp. 273–278 (2014)

    Google Scholar 

  4. Wirkuttis, N., Klein, H.: Artificial intelligence in cybersecurity. Cyber Intell. Secur. J. 1(1), 21–23 (2017)

    Google Scholar 

  5. Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, (Austin, Texas), pp. 2383–2392. Association for Computational Linguistics, November 2016

    Google Scholar 

  6. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, (Red Hook, NY, USA), pp. 6000–6010. Curran Associates Inc. (2017)

    Google Scholar 

  7. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), (Minneapolis, Minnesota), pp. 4171–4186. Association for Computational Linguistics, June 2019

    Google Scholar 

  8. Liu, Y.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv:1907.11692 (2019)

  9. Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_6

  10. Singh, A.K., Kumar, P.R.: A comparative study of page ranking algorithms for information retrieval. Int. J. Electr. Comput. Eng. 4, 469–480 (2009)

    MathSciNet  Google Scholar 

  11. Qaiser, S., Ali, R.: Text mining: use of TF-IDF to examine the relevance of words to documents. Int. J. Comput. Appl. 181(1), 25–29 (2018)

    Google Scholar 

  12. Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems : a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)

    Article  Google Scholar 

  13. Neto, J.A., Santos, A.D., Kaestner, C.A., Freitas, A.A.: Document clustering and text summarization. In: Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 41–55. The Practical Application Company (2000)

    Google Scholar 

  14. Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906 (2020)

  15. Lee, K., Chang, M.-W., Toutanova, K.: Latent retrieval for weakly supervised open domain question answering. arXiv preprint arXiv:1906.00300 (2019)

  16. Kwiatkowski, T., et al.: Natural questions: a benchmark for question answering research. Trans. Assoc. Comput. Linguist. 7, 453–466 (2019)

    Article  Google Scholar 

  17. Ge, L., Moh, T.: Improving text classification with word embedding. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 1796–1805 (2017)

    Google Scholar 

  18. Mikolov, T., Sutskever, I., Chen, J., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546 (2013)

  19. Yang, W., et al.: End-to-end open-domain question answering with bertserini. arXiv preprint arXiv:1902.01718 (2019)

  20. Haystack (2020). https://haystack.deepset.ai/. Accessed 06 June 2021

  21. Branden Chan, M.P., Möller, T., Soni, T.: Deepset roberta-base-squad2. https://huggingface.co/deepset/roberta-base-squad2. Accessed 06 May 2021

  22. Morla, R.: Ten AI stepping stones for cybersecurity. arXiv:1912.06817 (2019)

  23. Kayan, H., Nunes, M., Rana, O., Burnap, P., Perera, C.: Cybersecurity of industrial cyber-physical systems: a review, January 2021. arXiv e-prints arXiv:2101.03564

  24. Gardner, C., Waliga, A., Thaw, D., Churchman, S.: Using camouflaged cyber simulations as a model to ensure validity in cybersecurity experimentation. arXiv:1905.07059 (2019)

  25. Priya, V., Thaseen, I.S., Gadekallu, T.R., Aboudaif, M.K., Nasr, E.A.: Robust attack detection approach for IIoT using ensemble classifier. Comput. Mater. Continua 66(3), 2457–2470 (2021)

    Article  Google Scholar 

  26. Shah, S.A.R., Issac, B.: Performance comparison of intrusion detection systems and application of machine learning to SNORT system. Future Gener. Comput. Syst. 80, 157–170 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

The present work has been developed under the EUREKA ITEA3 Project CyberFactory#1 (ITEA-17032) and Project CyberFactory#1PT (ANI|P2020 40124) co-funded by Portugal 2020.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nuno Oliveira .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oliveira, N., Sousa, N., Praça, I. (2022). A Search Engine for Scientific Publications: A Cybersecurity Case Study. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_11

Download citation

Publish with us

Policies and ethics