@article{ART003246063},
author={Ji-Hyeok Choi and Kyu-Cheol Cho},
title={Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2025},
volume={30},
number={9},
pages={99-109}
TY - JOUR
AU - Ji-Hyeok Choi
AU - Kyu-Cheol Cho
TI - Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns
JO - Journal of The Korea Society of Computer and Information
PY - 2025
VL - 30
IS - 9
PB - The Korean Society Of Computer And Information
SP - 99
EP - 109
SN - 1598-849X
AB - With the recent surge in cybercrime, effective response and prediction have become increasingly important. This study proposes a predictive model for predicting cybercrime incidents and arrest counts through time series analysis. In this research, we utilized Seasonal Autoregressive Integrated Moving Average (SARIMA) and Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) models to analyze the trends and patterns of cybercrime occurrences, applying hyperparameter tuning to identify the optimal predictive variables. To evaluate the performance of each model, we used Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Akaike Information Criterion (AIC) metrics to compare model accuracy and derive the optimal model. The results demonstrate that the proposed model can effectively predict cybercrime incidents and arrests, providing a valuable tool for anticipating future cybercrime risks and informing preventive strategy development.
KW - Cybercrime Prediction;Time Series Analysis;SARIMA;ARIMAX;Hyperparameter Tuning
DO -
UR -
ER -
Ji-Hyeok Choi and Kyu-Cheol Cho. (2025). Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns. Journal of The Korea Society of Computer and Information, 30(9), 99-109.
Ji-Hyeok Choi and Kyu-Cheol Cho. 2025, "Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns", Journal of The Korea Society of Computer and Information, vol.30, no.9 pp.99-109.
Ji-Hyeok Choi, Kyu-Cheol Cho "Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns" Journal of The Korea Society of Computer and Information 30.9 pp.99-109 (2025) : 99.
Ji-Hyeok Choi, Kyu-Cheol Cho. Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns. 2025; 30(9), 99-109.
Ji-Hyeok Choi and Kyu-Cheol Cho. "Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns" Journal of The Korea Society of Computer and Information 30, no.9 (2025) : 99-109.
Ji-Hyeok Choi; Kyu-Cheol Cho. Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns. Journal of The Korea Society of Computer and Information, 30(9), 99-109.
Ji-Hyeok Choi; Kyu-Cheol Cho. Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns. Journal of The Korea Society of Computer and Information. 2025; 30(9) 99-109.
Ji-Hyeok Choi, Kyu-Cheol Cho. Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns. 2025; 30(9), 99-109.
Ji-Hyeok Choi and Kyu-Cheol Cho. "Cybercrime Incident and Arrest Prediction Model Through Time Series Analysis: Prediction of Trends and Patterns" Journal of The Korea Society of Computer and Information 30, no.9 (2025) : 99-109.
