[1]HU Jinming,HU Xiaofeng,SHI Lei,et al.Method of unauthorized intrusion scenario simulation in super high-rise building based on reinforcement learning[J].CAAI Transactions on Intelligent Systems,2025,20(4):958-968.[doi:10.11992/tis.202408002]
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Method of unauthorized intrusion scenario simulation in super high-rise building based on reinforcement learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 958-968 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Method of unauthorized intrusion scenario simulation in super high-rise building based on reinforcement learning
Author(s):
HU Jinming1;  HU Xiaofeng1; 2; 3;  SHI Lei4;  SHI Tuo5;  TENG Teng1
1. School of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China;
2. Center for Capital Social Safety, People’s Public Security University of China, Beijing 100038, China;
3. Key Laboratory of Security Technology & Risk Assessment, Ministry of Public Security, Beijing 102623, China;
4. State Key Laboratory of Media Integration and Communication, Communication University of China, Beijing 100024, China;
5. Department of Public Security Management, Beijing Police College, Beijing 102202, China
Keywords:
unauthorized intrusion; scenario simulation; super high-rise building; reinforcement learning; Bayesian network; security system; SARSA model; nonlinear regression
CLC:
TP18; X937
DOI:
10.11992/tis.202408002
Abstract:
To calculate the “optimal” intrusion path of potential illegal intruders in super high-rise buildings, a scenario simulation method based on reinforcement learning is proposed in the paper. This method provides a precise basis for efficiently preventing illegal access in super high-rise buildings by abstracting the buildings’ public corridors into a topological structure, calculating the probability of an intruder passing through each node based on a Bayesian network, and exploring the optimal intrusion path by means of reinforcement learning algorithms. To validate this method, a super high-rise building in the CBD area of Beijing was taken as an example, where the intrusion endpoint was assumed as the top floor and three different intrusion scenarios were designed. Results reveal that the SARSA model has the best training performance in the initial state (without any optimization measures). After optimizing the security system, increasing security system investment at interfloor nodes within the building is the most effective. In this context, a nonlinear fit between security investment and risk values shows that as investment in a security prevent system increases, intrusion risk remarkably decreases.
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