Next-Event Prediction in Cybercrime Complaint Narratives Using Temporal Event Scene Graphs

Mohammad Saad Rafeeq, Narendra Bijarniya, Chandramani Chaudhary

2025

Abstract

Cybercrime complaint narratives encompass complex sequences of criminal activities that challenge conventional sequence modeling techniques. This work introduces a framework that employs dynamic temporal event scene graphs to represent each narrative as an evolving, structured network of entities and events. Our approach converts complaint texts into temporal event scene graphs in which nodes symbolize key entities and edges capture interactions, annotated with their sequential order. This structured representation provides a richer and more intuitive understanding of how cybercrime incidents unfold over time. To forecast missing or forthcoming events, we fine-tune a pre-trained BART model using a masked sequence-to-sequence paradigm.Our experiments are performed on a dataset comprising thousands of real-world cybercrime reports, containing roughly 76,000 distinct event descriptions—a scale that introduces significant sparsity and generalization challenges. Our results demonstrate that while models such as GPT-2 and T5 struggle to capture robust patterns in this diverse domain, the BART-based approach achieves modest yet promising improvements.

Paper Citation

in Harvard Style

Rafeeq M., Bijarniya N. and Chaudhary C. (2025). Next-Event Prediction in Cybercrime Complaint Narratives Using Temporal Event Scene Graphs. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 693-700. DOI: 10.5220/0013647000003967

in Bibtex Style

@conference{data25,
author={Mohammad Rafeeq and Narendra Bijarniya and Chandramani Chaudhary},
title={Next-Event Prediction in Cybercrime Complaint Narratives Using Temporal Event Scene Graphs},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={693-700},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013647000003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Next-Event Prediction in Cybercrime Complaint Narratives Using Temporal Event Scene Graphs
SN - 978-989-758-758-0
AU - Rafeeq M.
AU - Bijarniya N.
AU - Chaudhary C.
PY - 2025
SP - 693
EP - 700
DO - 10.5220/0013647000003967
PB - SciTePress