A Deception Defense Timing Selection Method Based on Time-Delayed FlipIt Game in Cloud-Edge Collaborative Networks
Jinchuan Pei1, Yuxiang Hu1,2,3,4,*, Hongtao Yu1, Zihao Wang1, Menglong Li1,2,3,4
1 Information Engineering University, Zhengzhou, China
2 Key Laboratory of Cyberspace Endogenous Security of Henan Province, Zhengzhou, China
3 Key Laboratory of Cyberspace Security Ministry of Education of China, Zhengzhou, China
4 National Key Laboratory of Advanced Communication Networks, Shijiazhuang, China
* Corresponding Author: Yuxiang Hu. Email: 
(This article belongs to the Special Issue: Advancing Edge-Cloud Systems with Software-Defined Networking and Intelligence-Driven Approaches)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.079684
Received 26 January 2026; Accepted 25 March 2026; Published online 15 April 2026
Abstract
In the cloud-edge collaborative network, advanced persistent threats (APTs) pose a serious security risk to critical network assets. Although network deception defense can mislead attackers’ cognition, its effectiveness depends on dynamically selecting appropriate rotation timings of the deception defense. However, the deployment of deception resources and state updates is not completed instantaneously, and existing methods ignore the state transition delay and the dynamic interaction between the attackers and defenders during the real attack and defense process. To address this, we propose a deception defense timing selection method based on the time-delayed FlipIt game. Firstly, a network state evolution model integrating state transition delay is constructed, and the dynamic transfer process between node states is characterized by a set of delay differential equations. Secondly, a cloud-edge collaborative defense architecture is designed. On this basis, a time-delayed FlipIt game model (TD-FlipIt) is established, and the gate control mechanism is introduced to formalize the defense cooling period as a constraint for the rotation action of deception resources. Subsequently, we use the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the rotation strategy for deception defense timing. Experimental results show that the proposed method can effectively optimize the selection of defense timing, ensuring defense effectiveness while reducing resource consumption, and providing effective support for defense in the cloud-edge collaborative environment.
Keywords
Cloud-edge collaborative network; deception defense timing; FlipIt game; multi-agent reinforcement learning
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