Publication detail

Job Adverts Analyzer for Cybersecurity Skills Needs Evaluation

RICCI, S. SIKORA, M. PARKER, S. LENDAK, I. DANIDOU, Y. CHATZOPOULOU, A. BADONNEL, R. ALKSNYS, D.

Original Title

Job Adverts Analyzer for Cybersecurity Skills Needs Evaluation

Type

conference paper

Language

English

Original Abstract

This article presents a new free web-based application, the Cybersecurity Job Ads Analyzer, which has been created to collect and analyse job adverts using a machine learning algorithm. This algorithm enables the detection of the skills required in advertised cybersecurity work positions. The application is both interactive and dynamic allowing for automated analyses and for the underlying database of job adverts to be easily updated. Through the Cybersecurity Job Ads Analyzer, it is possible to explore the skills required over time, and thereby enable academia and other training providers to better understand and address the needs of the industry. We will describe in detail the user interface and technical background of the application, as well as highlight the preliminary statistical results we have obtained from analysing the current database of job adverts.

Keywords

Cybersecurity Education;Skills;Work Roles;Machine Learning;Job Ads Analyzer

Authors

RICCI, S.; SIKORA, M.; PARKER, S.; LENDAK, I.; DANIDOU, Y.; CHATZOPOULOU, A.; BADONNEL, R.; ALKSNYS, D.

Released

23. 8. 2022

Publisher

ACM

ISBN

978-1-4503-9670-7

Book

ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security

Pages from

1

Pages to

10

Pages count

10

URL

https://dl.acm.org/doi/10.1145/3538969.3543821

BibTex

@inproceedings{BUT178195,
  author="Sara {Ricci} and Marek {Sikora} and Simon {Parker} and Imre {Lendak} and Yianna {Danidou} and Argyro {Chatzopoulou} and Remi {Badonnel} and Donatas {Alksnys}",
  title="Job Adverts Analyzer for Cybersecurity Skills Needs Evaluation",
  booktitle="ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security",
  year="2022",
  pages="1--10",
  publisher="ACM",
  doi="10.1145/3538969.3543821",
  isbn="978-1-4503-9670-7",
  url="https://dl.acm.org/doi/10.1145/3538969.3543821"
}