Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model

Abstract

Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy.

Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture

“…In this way, future results are influenced by past input values, and it is widely used in various time series prediction studies [37][38][39]. It is also widely used for predicting the performance data of communication networks that change over time [40][41][42][43].…”