物联网安全与隐私保护中博弈论的应用综述

doi:10.12305/j.issn.1001-506X.2026.03.31

摘要/Abstract

摘要：

物联网的安全与隐私保护是研究热点和技术难题。博弈论方法能够有效分析物联网系统中多方利益主体的博弈行为，在决策分析层面对防御技术形成重要补充。综述了近五年来博弈论在物联网安全与隐私保护中的应用，系统梳理了其在安全对抗中的应用场景与技术贡献。按照对抗主体的不同，相关研究分为两类：一类聚焦攻防双方的博弈，另一类针对特定物联网场景中不同组织之间的交互与协作。深入讨论了当前研究的挑战，包括均衡假设的合理性、计算复杂性以及算法实用性等问题，并展望了结合强化学习技术开发智能博弈安全机制的发展方向。

关键词: 物联网安全, 隐私保护, 博弈论, 纳什均衡, 强化学习, 智能安全机制

Abstract:

The security and privacy protection of the Internet of Things is a prominent research hotspot and a challenging technical issue. Game theory effectively analyzes the interactions among multiple stakeholders in Internet of Things systems and provides critical theoretical support for decision-making in defense technologies. The applications of game theory in Internet of Things security and privacy protection over the past five years are reviewed, systematically summarizing its application scenarios and technical contributions in security confrontations. Based on different adversarial entities, the related research is categorized into two types: dynamic games between attackers and defenders, and interactive games among different organizations in specific Internet of Things scenarios. The current challenges are explored, such as the rationality of equilibrium assumptions, computational complexity, and practical applicability of algorithms. Finally, future research directions are outlined, focusing on integrating reinforcement learning to develop intelligent game-based security mechanisms.

Key words: Internet of Things security, privacy protection, game theory, Nash equilibrium, reinforcement learning, intelligent security mechanism

中图分类号:

金志刚, 刘泽培, 武晓栋. 物联网安全与隐私保护中博弈论的应用综述[J]. 系统工程与电子技术, 2026, 48(3): 1072-1082.

Zhigang JIN, Zepei LIU, Xiaodong WU. Survey of game theory applications in Internet of Things security and privacy[J]. Systems Engineering and Electronics, 2026, 48(3): 1072-1082.

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IoT特性	代表场景	额外安全隐私风险	博弈论方法优势
物理与网络强耦合	CPS	增大安全暴露面，传统技术防御不适用	战略分析，灵活的控制策略
资源受限	边缘计算	针对边缘传感器的DDoS攻击更具威胁，难以部署传统强健的防御技术	高效的资源配置策略，动态的任务卸载方案
隐私性	MCS	协议内生漏洞，多实体利益互动	弹性的隐私保护方案，灵活的激励机制

模型分类	模型名称	主要特征
非合作博弈模型	零和博弈	博弈各方的收益之和总是等于“0”
	Stackelberg模型	领导者先行动，追随者根据领导者的行动做出自己的选择
	贝叶斯博弈	至少有一名博弈者对其他博弈者的收益不完全知情
	Potential博弈	所有博弈者改变策略的动机可以用势函数来表示
	随机博弈	随机博弈是一种按阶段顺序进行的重复博弈，具有概率过渡性
合作博弈模型	重复博弈	多次或无限次重复博弈时，博弈者通过契约看重更长远的收益
	合作博弈	博弈者可以通过结成联盟并根据贡献分配收益
	讨价还价博弈	博弈者达成可执行的协议，协商如何分配收益

博弈模型	IoT应用场景	研究问题	点评
贝叶斯博弈	边缘计算^[25]	任务卸载中的信息不对称问题	强调信息不对称条件下的任务调度优化，提升负载均衡性和抗攻击能力
Potential博弈	边缘计算^[26]	DDoS攻击缓解的成本最小化	适用于静态场景，对高动态环境的适应性不足
Stackelberg博弈	无线网络^[27] 切换系统^[28]	功率分配优化和异步切换中的资源竞争	适合层次化决策场景（如功率调度），但计算复杂度较高
零和随机博弈	多通道网络系统^[30,33,35]	动态传输调度与攻击防御策略	动态学习方法显著增强对动态环境的适应性，适合复杂通信场景
双人零和博弈	CPS^[31,34]	无模型控制与能量差异化防御	数据驱动的控制策略有效性^[31]，适应动态CPS场景^[34]
静态合作博弈与微分博弈	CPS^[36]	多攻击者协作破坏CPS稳定性，跨层建模分析攻击协作与防御竞争	结合物理层与网络层特性，揭示多攻击者情景下的系统对抗潜力
多阶段信号博弈	一般IoT^[37]	主动防御框架结合移动目标防御和网络欺骗，动态构建自适应网络属性	动态构建网络属性，有效缓解攻击影响，适用于需要快速适应的场景

IoT应用	攻防互动场景	博弈模型	点评
CPS	低交互与高交互蜜罐部署^[38]	贝叶斯博弈	考虑了资源和人工成本的限制
智能电网	低交互和高交互蜜罐并结合前景理论^[39]	静态贝叶斯博弈	分析有限理性影响但只是在静态场景下
工业IoT	动态有限理性结合SDN对抗APT^[41]	进化博弈	进化稳定策略考虑有限理性有效分析动态场景
智能电网	博弈决策分析与IDS^[40]	非合作博弈与贝叶斯博弈	实现不完全信息下的动态配置策略
云计算	结合IDS的基于签名、异常和蜜罐的联合检查^[42]	非合作博弈	实现动态高效的联合防御系统
一般IoT	有欺骗证据的蜜罐博弈^[44]	基于信念的有限博弈	综合考虑博弈全过程以及攻击的持续性和动态性
一般IoT	攻击者与防御方SSH连接^[43]	不完全信息动态博弈	结合RL算法实现了策略的快速求解

IoT应用	激励动机	博弈模型	点评
群智感知	鼓励用户参与感知应用^[53]	Stackelberg博弈	体现引入了社交结构的有效性
群智感知	鼓励用户诚实参与同时保护隐私^[54]	非合作博弈	机制保证诚实参与是用户最优策略
云服务	激励参与云，抑制不诚实云服务商^[55]	重复博弈	博弈论结合信誉有效约束了不诚实行为
智能交通	数据持有者和请求者的利益互动^[56]	马尔可夫博弈	模拟动态地车辆网中的数据请求
FL	用户灵活确定隐私预算^[51]	Stackelberg博弈	实现灵活的隐私保护方案
	数据质量激励与通过功率保护隐私^[57]	Stackelberg博弈	考虑了功耗、数据质量与隐私泄露之间的权衡
	鼓励用户参与限制恶意用户影响^[58]	拍卖博弈	考虑了支配策略真实性与个体合理性
	能源安全和信息隐私联合保护框架^[59]	非合作博弈	实现鼓励参与和信息保护之间的权衡