Simona Rudohradská
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Simona Rudohradská has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dávid Varga
Faculty of Science, Pavol Jozef šafárik University in Košice
1 shared publication - Laura Bachňáková Rózenfeldová
Faculty of Law, Pavol Jozef Šafárik University in Košice
1 shared publication - Pavol Sokol
Pavol Jozef Šafárik University in Košice
1 shared publication - Regina Hučková
Faculty of Law, Pavol Jozef Šafárik University in Košice
1 shared publication
Latest Publications
- Open Access
- 2 December 2025
- Published by IOS Press
Anomaly Detection in Legal Text – A Case Study on Cybersecurity Contracts
The increasing availability of legal documentation provides opportunities for applying empirical research methods that enable a comprehensive analysis of complex legal data. Systematic Content Analysis offers a structured approach for examining legal texts. This paper examines the possibility of anomaly detection in legal text through the employment of machine learning methods. The conclusions presented are formulated on a basis of a case study focusing on contractual agreements concluded between public authorities and private service providers in the field of cybersecurity. In this regard, we propose an approach to the detection of anomalous legal text, providing a categorization of attributes that impact the determination of anomalies and describing their significance in the selected domain.
- 2 December 2025
- Published by IOS Press
Open AccessAnomaly Detection in Legal Text – A Case Study on Cybersecurity Contracts
The increasing availability of legal documentation provides opportunities for applying empirical research methods that enable a comprehensive analysis of complex legal data. Systematic Content Analysis offers a structured approach for examining legal texts. This paper examines the possibility of anomaly detection in legal text through the employment of machine learning methods. The conclusions presented are formulated on a basis of a case study focusing on contractual agreements concluded between public authorities and private service providers in the field of cybersecurity. In this regard, we propose an approach to the detection of anomalous legal text, providing a categorization of attributes that impact the determination of anomalies and describing their significance in the selected domain.