Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks

Abstract

Online smartphone game bots can cause unfair behaviors and even shorten the game’s life cycle. The random forest algorithm in machine learning is a widely used solution to identify game bots through behavioral features. Although the random forest algorithm can exactly detect more definite game bot players, some players belonging to the gray area cannot be detected accurately. Therefore, this study collects players’ data and extracts the features to build the multilayer perceptron, neural network model, for effectively detecting online smartphone game bots. This approach calculates each player’s abnormal rate to judge game bots and is evaluated on the famous mobile online game. Based on these abnormal rates, we then use K means to cluster players and further define the gray area. In the experimental evaluation, the results demonstrate the proposed learning model has better performance, not only increasing the accuracy but also reducing the error rate as compared with the random forest model in the same players’ dataset. Accordingly, the proposed learning model can detect bot players more accurately and is feasible for real online smartphone games.

Last-Mile Optimization Using Neural Networks

“…Tsaur et al (2022) [16] investigated detecting game bots in a mobile online game by using MLP neural networks. The results demonstrated that the proposed approach achieves a higher accuracy of 99.894% compared to the widely used RF algorithm.…”

“…However, the capabilities of ML approaches such as KNN, SVM, DT, and RF are analyzed and compared to MLP. [27] 2016 E-Commerce path-wise, node-wise, and depth-wise Zewairi et al [12] 2017 Network Traffic MLP Parsaei et al [14] 2017 Network Traffic NB, Levenberg-Marquardt, and MLP Akritidis et al [25] 2018 E-Commerce LR and RF Obaid et al [8] 2018 Medical diagnosis SVM, KNN, and DT Miller et al [11] 2018 Network Traffic MLP Xu et al [24] 2019 E-Commerce L2AC Ranjeeth et al [23] 2019 Education MLP Alarsan and Younes [4] 2019 Medical diagnosis DT, RF, and GBT Tadesse et al [21] 2019 NLP LR, SVM, Ada Boost, RF, and MLP Muthuselvan et al [9] 2019 Medical diagnosis RT, NB, and K-star Shanmughasundaram et al [17] 2020 Electric MLP Servos et al [35] 2020 Logistics Extra Trees, AdaBoost, and SVR Rouari et al [28] 2021 Logistics CNN Jonathan et al [13] 2021 Network Traffic NB, KNN, SVM, and DT Azad et al [3] 2021 Medical diagnosis MLP Churcher et al [15] 2021 Network Traffic KNN, SVM, DT, NB, RF, and ANN Megdad et al [20] 2022 Finance MLP, RF, NB, LGBM, Ada Boost, KNN, LR, and MLP Taylor et al [36] 2021 Logistics SVR Hassan et al [5] 2022 Medical diagnosis LR, MLP, RF, and DT Tsaur et al [16] 2022 Online Game MLP Cui et al [7] 2022 Medical diagnosis MLP Kasasbeh et al [19] 2022 Finance MLP Qu and Li [31] 2022 Logistics Ant colony Cao [33] 2022 Logistics CNN-GRU Salais-Fierro and Martínez [34] 2022 Logistics 2022…”

Retracted: Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”