FingFormer: Contrastive Graph-based Finger Operation Transformer for Unsupervised Mobile Game Bot Detection

Li, Wenbin; Chu, Xiaokai; Su, Yueyang; Yao, Di; Zhao, Shankun; Wu, Runze; Zhang, Shize; Tao, Jianrong; Deng, Hao; Bi, Jun

doi:10.1145/3485447.3512272

Cited by 7 publications

(2 citation statements)

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“…There has been a lot of research [2], [5]- [7], [57], [58] on trajectory outlier detection and existing methods can be divided into two categories, i.e., metric-based methods and learning-based methods. The metric-based methods [8], [9], [13] identify anomalous trajectories based on their distance from other trajectories or reference trajectories.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Region-Wide Trajectory Outlier Detection

2023

IEEE Access

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Trajectory outlier detection is a crucial task in trajectory data mining and has received significant attention. However, the distribution of trajectories is tied to social activities, resulting in extreme unevenness among regions. While existing methods have demonstrated excellent performance in regions with sufficient historical trajectories, they frequently struggle to detect outliers in regions with limited trajectories. Unfortunately, this issue has not received much attention, leaving a gap in the current understanding of trajectory mining. To deal with this problem, we in this paper propose a model called TTOD that can effectively detect outliers in regions with sparse data by transferring knowledge among regions. The main idea is to learn a feature mapping function that maps the global feature space of auxiliary regions to the target region's specific feature space. To achieve this, we adopt a VAE-based model called the Global VAE to learn the global feature space in auxiliary regions by modeling the trajectory patterns with Gaussian distributions. Then, we propose a Specific-region VAE that serves as the mapping function to learn the target feature space. Additionally, considering the data drift of feature distributions among regions, we introduced an additional pattern synthesis layer, named the De-drift Layer, to diversify the target feature space, thus addressing the pattern missing issue caused by the gap of feature distributions between the auxiliary regions and the target regions. Then the target feature space can be well studied and applied to detect outliers. Finally, we conduct extensive experiments on two real taxi trajectory datasets and the results show that TTOD achieves state-ofthe-art performance compared with the baselines.INDEX TERMS trajectory outlier detection, transfer learning, VAE, spatial-temporal data, trajectory data mining.

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Section: Introductionmentioning

confidence: 99%

Transfer Learning for Region-Wide Trajectory Outlier Detection

2023

IEEE Access

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“…Li and others [28] introduced a transformer‐style detection model called FingFormer. They focused on situations where there were no cheating data and performed a graph analysis of finger movements on a sensor.…”

Section: Introductionmentioning

confidence: 99%

Quick and easy game bot detection based on action time interval estimation

Kang

Kim

2022

ETRI Journal

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Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

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