Detail publikace

Job Adverts Analyzer for Cybersecurity Skills Needs Evaluation

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

Originální název

Job Adverts Analyzer for Cybersecurity Skills Needs Evaluation

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

23. 8. 2022

Nakladatel

ACM

ISBN

978-1-4503-9670-7

Kniha

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

Strany od

1

Strany do

10

Strany počet

10

URL

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"
}