Auto-Classification of FIA-Cybercrime Wing Complaints Using Bidirectional Encoder Representations from Transformers Model
Year: 2025
Type: article
Abstract: The Cybercrime Wing (CCW) of the Federal Investigation Agency, which was formerlyknown as the National Response Center for Cybercrime (NR3C), is governed by rules that were created in2016 as part of the Prevention of Electronic Crimes Act (PECA) to combat cybercrime. Criminal activitiesexecuted using computers and the internet are referred to as cybercrimes. In order to carry out illegalactivities, cybercriminals make use of any information system as their primary means of communicationwith the devices that belong to their victims. This research mainly focusses on the cybercrime complaintswith an automated classification system. To achieve the automatic modelling for classification of differenttypes of cybercrimes, this study used the Bidirectional Encoder Representations from Transformers (BERT).Its obstacles mainly include the possibility of human errors as it manually classifies cybercrime complaints,also that there might be delays during handling in comparison with an automated system. The datasetincludes complaints submitted in English during a two years window, and it was encoded, tokenized andcleaned thoroughly. The purpose was to simplify the training process for the model. The study used alightly fine-tuned, pretrained (BERT)-base-uncased model. The findings confirm that the model can be usedfor classifying complaints and exhibits an excellent classification accuracy, precision and F1-scores betweendifferent cybercrime offences indicating its supremacy among advanced Natural language processing (NLP)techniques to strengthen cybersecurity measures.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408580623