This study addresses the challenge of creating datasets for cybercrime analysis while complying with the requirements of regulations such as the General Data Protection Regulation (GDPR) and Organic Law 10/1995 of the Penal Code. To this end, a system is proposed for collecting information from the Telegram platform, including text, audio, and images; the implementation of speech-to-text transcription models incorporating signal enhancement techniques; and the evaluation of different Named Entity Recognition (NER) solutions, including Microsoft Presidio and AI models designed using a transformer-based architecture. Experimental results indicate that Parakeet achieves the best performance in audio transcription, while the proposed NER solutions achieve the highest f1-score values in detecting sensitive information. In addition, anonymization metrics are presented that allow evaluation of the preservation of structural coherence in the data, while simultaneously guaranteeing the protection of personal information and supporting cybersecurity research within the current legal framework.
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports
Bosch Center for Artificial Intelligence（博世人工智能中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Hochschule der Medien（斯图加特传媒大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；Universität Augsburg（奥格斯堡大学）
On the Binding Problem in Artificial Neural Networks
Google Research（谷歌研究院）；Istituto Dalle Molle di studi sull’intelligenza artificiale (IDSIA)（达莱莫莱人工智能研究所（IDSIA））；Università della Svizzera Italiana (USI)（瑞士意大利语区大学（USI））；Scuola universitaria professionale della Svizzera italiana (SUPSI)（瑞士意大利语区应用科学大学（SUPSI））
Knowledge mining of unstructured information: application to cyber-domain
Aalto University School of Science（阿尔托大学理学院）；University of Jyväskylä（于韦斯屈莱大学）；Cyberwatch Finland Oy（芬兰Cyberwatch公司）；The Alan Turing Institute（艾伦·图灵研究所）
An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System（苏瑞福公共卫生学院、新加坡国立大学及新加坡国立大学健康系统）；Singapore Civil Defence Force（新加坡民防部队）；National University Hospital（国立大学医院）；Home Team Science & Technology Agency（Home Team科技局）；Singapore General Hospital（新加坡中央医院）
DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization
Nanyang Technological University（南洋理工大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Hengxin Tech（恒心科技）
