Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Comput. Sci.

Sec. Networks and Communications

Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1670238

This article is part of the Research TopicResource Coordination and Joint Optimization in Cloud-Edge-End SystemsView all 3 articles

Market Malicious Bidding User Detection Based on Multi-Agent Reinforcement Learning

Provisionally accepted
Peng  WangPeng Wang1Yilin  ZhangYilin Zhang1Yin  LanYin Lan1Ziyang  HuangZiyang Huang1Di  TangDi Tang1Yu  LiangYu Liang2*
  • 1Hunan First Normal University, Changsha, China
  • 2Shenzhen Polytechnic University, Shenzhen, China

The final, formatted version of the article will be published soon.

With the rapid growth of e-commerce and online auction markets, malicious bidding activities have severely disrupted market order. Traditional detection methods face limitations due to their inability to effectively address the covert nature, dynamic characteristics, and massive data volumes associated with such behaviors. To address this challenge, this paper proposes a detection method for users engaging in malicious bidding based on Multi-Agent Reinforcement Learning (MARL). This approach first models target users as specialized agents, then integrates their historical bidding data, and finally learns optimal strategies through competitive games with adversarial agents. Additionally, this paper designs a dynamic adjustment mechanism for the maliciousness coefficient to simulate user behavior changes, enabling precise assessment of malicious intent. Compared to existing fraud detection approaches based on reinforcement learning, the fundamental innovation lies not merely in applying MARL technology, but in introducing the novel "dynamic maliciousness coefficient" mechanism. This mechanism achieves dynamic and precise maliciousness assessment through mathematical modeling and real-time iteration, addressing the shortcomings of traditional MARL models in capturing user behavioral heterogeneity. Experimental results demonstrate that this method exhibits higher detection accuracy and adaptability in complex dynamic market environments. It effectively captures bidder interaction relationships, significantly enhancing the detection of malicious behavior.

Keywords: Bidding detection, Dynamic Maliciousness Coefficient, Malicious Bidding, Market order, Multi-AgentReinforcement Learning

Received: 21 Jul 2025; Accepted: 17 Sep 2025.

Copyright: © 2025 Wang, Zhang, Lan, Huang, Tang and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yu Liang, eungyu.ac@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.