[1]ZHOU Jinghao,SHI Lei,SHI Tuo,et al.Spatiotemporal prediction of telecommunications network fraud crime with multidimensional feature fusion[J].CAAI Transactions on Intelligent Systems,2025,20(5):1112-1122.[doi:10.11992/tis.202412025]
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Spatiotemporal prediction of telecommunications network fraud crime with multidimensional feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 5 Page number: 1112-1122 Column: 学术论文—机器学习 Public date: 2025-09-05
Title:
Spatiotemporal prediction of telecommunications network fraud crime with multidimensional feature fusion
Author(s):
ZHOU Jinghao1;  SHI Lei2;  SHI Tuo3; 4;  CHEN Peng1
1. School of Information Network Security, People’s Public Security University of China, Beijing 102600, China;
2. State Key Laboratory of Media Integration and Communication, Communication University of China, Beijing 100024, China;
3. Department of Public Security Management, Beijing Police College, Beijing 102202, China;
4. Standard Laboratory of Police Data and Intelligence of Beijing Public Security Bureau, Beijing Police College, Beijing 102202, China
Keywords:
spatiotemporal fraud prediction; multidimensional features; spatio-temporal feature fusion; spatial environmental feature factors; graph attention networks; time-sliding window; iTransformer
CLC:
TP391.41
DOI:
10.11992/tis.202412025
Abstract:
Spatiotemporal prediction of telecommunications fraud crimes can substantially enhance targeted antifraud efforts. However, existing methods suffer from poor performance due to sparse and periodic incident time-series data, as well as the heterogeneity of spatial environmental factors. Aiming to address these challenges, this paper proposes a multidimensional feature-integrated telecom fraud spatiotemporal prediction (MF-TSP) model. First, a spatial feature selection module was constructed by integrating regional topological graphs to effectively incorporate neighborhood crime patterns. A time-sliding window technique, combined with a multidimensional temporal feature extraction module and an inverted Transformer, addresses data sparsity while capturing periodicity, global dependencies, and complex multivariate correlations. Furthermore, deep spatiotemporal fusion and nonlinear mapping notably improve prediction accuracy. Experiments on real-world telecom fraud data from City B demonstrate that MF-TSP outperforms seven baseline models under three different input time-step conditions.
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