Non-Intrusive Graph-Based Bot Detection for E-Commerce Using Inductive Graph Neural Networks

• URL: http://arxiv.org/abs/2601.22579v1
• Date: Fri, 30 Jan 2026 05:21:32 GMT
• Title: Non-Intrusive Graph-Based Bot Detection for E-Commerce Using Inductive Graph Neural Networks
• Authors: Sichen Zhao, Zhiming Xue, Yalun Qi, Xianling Zeng, Zihan Yu,
• Abstract summary: Malicious bots pose a growing threat to e-commerce platforms by scraping data, hoarding inventory, and perpetrating fraud.<n>Traditional bot mitigation techniques, including IP blacklists and CAPTCHA-based challenges, are increasingly ineffective or intrusive.<n>This work proposes a non-intrusive graph-based bot detection framework for e-commerce that models user session behavior through a graph representation.
• Score: 4.230025065044209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malicious bots pose a growing threat to e-commerce platforms by scraping data, hoarding inventory, and perpetrating fraud. Traditional bot mitigation techniques, including IP blacklists and CAPTCHA-based challenges, are increasingly ineffective or intrusive, as modern bots leverage proxies, botnets, and AI-assisted evasion strategies. This work proposes a non-intrusive graph-based bot detection framework for e-commerce that models user session behavior through a graph representation and applies an inductive graph neural network for classification. The approach captures both relational structure and behavioral semantics, enabling accurate identification of subtle automated activity that evades feature-based methods. Experiments on real-world e-commerce traffic demonstrate that the proposed inductive graph model outperforms a strong session-level multilayer perceptron baseline in terms of AUC and F1 score. Additional adversarial perturbation and cold-start simulations show that the model remains robust under moderate graph modifications and generalizes effectively to previously unseen sessions and URLs. The proposed framework is deployment-friendly, integrates with existing systems without client-side instrumentation, and supports real-time inference and incremental updates, making it suitable for practical e-commerce security deployments.

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