TY  - EJOU
AU  - Alsharabi, Naif 
AU  - Bhardwaj, Akashdeep 

TI  - Analyzing Human Trafficking Networks Using Graph-Based Visualization and ARIMA Time Series Forecasting
T2  - Journal of Cyber Security

PY  - 2025
VL  - 7
IS  - 1
SN  - 2579-0064

AB  - In a world driven by unwavering moral principles rooted in ethics, the widespread exploitation of human beings stands universally condemned as abhorrent and intolerable. Traditional methods employed to identify, prevent, and seek justice for human trafficking have demonstrated limited effectiveness, leaving us confronted with harrowing instances of innocent children robbed of their childhood, women enduring unspeakable humiliation and sexual exploitation, and men trapped in servitude by unscrupulous oppressors on foreign shores. This paper focuses on human trafficking and introduces intelligent technologies including graph database solutions for deciphering unstructured relationships and entity nodes, enabling the comprehensive visualization of datasets linked to the scourge of human trafficking. The authors propose an autoregressive integrated moving average (ARIMA) time series model, showcasing its remarkable accuracy in forecasting temporal trends within the dataset’s timeframe. The research analysis leverages a comprehensive human trafficking dataset from Kaggle, comprising 48,800 records encompassing 63 distinct attributes. The findings underscore the effectiveness of the proposed model, revealing a compelling correlation between the predictions generated by the model and the actual values embedded in the dataset.
KW  - Human trafficking; human trafficking; graph database; link analysis time series; autoregressive; integrated moving; ARIMA

DO  - 10.32604/jcs.2025.064019