无线Mesh网络中激励机制研究

为了激励无线Mesh网络中自私节点转发数据,通过分析节点自私行为激励的原因和目标,将适用于无线Mesh网络中对节点自私行为的四类激励机制进行比较.讨论了无线Mesh网络博弈模型,并对无线Mesh网络中重复博弈模型进行理论分析.针对节点的自私行为提出了基于博弈论与信誉相结合的激励方案.仿真实验表明,该方案不仅降低了基于信誉的复杂度又能有效激励更多自私节点转发数据,从而提高了网络的整体性能.     

基于博弈论和网络弱点分析的网络主动防御技术研究

针对各种网络攻击,传统的安全技术大部分属于静态的、片面的被动安全防御,各种技术孤立使用,不能很好地配合,防御滞后于攻击,缺乏主动性和对攻击的预测能力。面对这两个问题,综合使用多种防御措施,基于博弈论最优决策方法,实现了在攻击发生前,对攻击步骤做到最优预测,并做好相应的防御准备,从而获得攻防的主动权。研究中,首先分析网络弱点信息,建立弱点信息关联图和基于弱点的系统状态转化图。同时捕获当前攻击行为模式串,预测并获取攻击者准弱点利用集。然后进一步建立系统状态转换博弈树,并对树节点进行权重标识,建立博弈论可求解的矩阵博弈模型。最后,利用线性规划知识求解该博弈模型,得到可能攻击行为概率分布和相应的防御措施最优概率分布,从而达到网络主动防御的目的。     

大型嵌入式网络中的节点信息安全预判技术研究

为了防护大型嵌入式网络中的节点信息安全,提出基于博弈论的安全预判模型,并对基于博弈论的大型嵌入式网络节点信息安全预判系统进行实现。介绍了博弈论中博弈者、博弈策略、期望函数的相互关系,取节点信息攻守双方为博弈者,构建四元组安全预判模型,给出与博弈策略行为有关的期望函数。当节点信息不安全时,根据非合作博弈均衡原理得到最佳博弈策略集合;当节点信息安全时,将期望函数作为安全预判结果输出。经过实验验证可知,所提基于博弈论的安全预判模型具有很好的节点信息传输性能和节点信息搜索性能。     

基于Markov攻击图和博弈模型的区块链安全态势感知方法

全面准确地感知区块链网络中各节点所遭受的日蚀攻击情况是一个难题,该文针对该难题提出一种基于Markov攻击图和博弈模型的区块链安全态势感知方法。该方法结合区块链网络各节点以及日蚀攻击的特点建立Markov攻击图模型,随后将该模型进行量化从而计算各攻击路径的转换概率,选择较高概率的攻击路径进行多阶段攻防博弈并计算双方的最大目标函数值。通过分析这些函数值,完成对整个区块链网络节点的安全态势感知,达到对未来安全情况的预测和系统维护的目的。实验对比表明,该模型方法不但具有较低的入侵成功次数,还具有较好的确保系统完整性等方面的优势。     

数字图像隐写容量的博弈分析

提出了一种基于数字图像的新的隐写信道模型.在该模型中,考虑隐写分析器的作用和载体与隐秘体的相似性,引入错误概率和检测率作为失真约束条件.运用博弈论,把隐写过程看成编解码方与攻击方的博弈,给出了隐写博弈的双方在不同失真约束条件下的隐写容量,并对容量结果进行了分析.     

基于随机演化博弈模型的网络防御策略选取方法

针对攻防博弈系统中存在攻防策略集和系统运行环境改变等各类随机干扰因素的问题,传统确定性博弈模型无法准确描述攻防博弈过程.利用非线性Itó随机微分方程构建随机演化博弈模型,用于分析攻防随机动态演化过程.通过求解,并根据随机微分方程稳定性判别定理对攻防双方的策略选取状态进行稳定性分析,设计出基于随机攻防演化博弈模型的安全防御策略选取算法.最后,通过仿真验证了不同强度的随机干扰对攻防决策演化速率的影响,且干扰强度越大,防御者更倾向于选择强防御策略,攻击者更倾向于选择强攻击策略.本文模型和方法能够用于网络攻击行为预测和安全防御决策.     

基于博弈论的无线传感器网络恶意程序传播模型

恶意程序传播是无线传感器网络（wireless sensor network,WSN）面临的一类重要安全问题。从博弈论的角度对WSN恶意程序传播的微观机理进行分析,建立了WSN的攻防博弈模型,求出了博弈模型的混合纳什均衡解,并根据博弈双方的混合纳什均衡策略确定恶意程序的传染概率,从而建立了WSN的恶意程序传播模型。通过使用元胞自动机方法对WSN的恶意程序传播过程进行模拟,揭示了恶意程序的传播速度与博弈参数之间的关系,研究结果对抑制WSN恶意程序传播具有理论指导意义。     

车载自组网节点轨迹隐私攻防博弈模型

针对主动攻击所发布车辆轨迹隐私的场景,利用信息熵量化攻击者和防御者的能力,采用博弈论对车辆轨迹隐私攻击和防御进行建模,并给出攻击和防御策略,分析了攻防双方之间的博弈过程。通过对真实轨迹数据分析,得出完全信息博弈下的纳什均衡点和相应攻击策略下的最优防御策略。     

基于双矩阵博弈的入侵检测模型

论文针对网络攻防中入侵者与入侵检测及其响应系统之间的关系,通过参考以往博弈模型,建立了一个基于双矩阵非零和博弈的入侵检测模型,并通过Lemke-Howson算法推导出整个博弈的最优混合策略,且对攻防双方的成本进行分析,探讨了最优策略中各个因素之间的函数关系。     

基于博弈理论的无线自组织网增强协作模型研究

为了解决无线自组织网络中转发节点因自身能量与存储空间限制而拒绝协作的自私性问题,该文从分析数据包源节点与转发节点的收益与开销特性出发,基于虚拟货币的奖励机制,结合博弈理论提出无线自组织网络增强协作模型。该模型将网络协作问题转化为数据包转发路径中多转发节点与源节点收益的博弈均衡问题,在保障双方利益的基础上提出最优的激励方式,促进通信协作的进行。另外,为最大化网络生存时间与避免拥塞,该模型对转发节点的电量与存储空间状态做了相应的约束。     

Website defense strategy selection method based on attack-defense game and Monte Carlo simulation

Aiming at the selection of security defense strategy in network attack-defense,the dynamic change process of mutual influence between attack-defense strategy was studied.Based on the game process of both offense and defense,the attack-defense game model was constructed,the attack process of the attacker based on Monte Carlo simulation was simulated and the attacker’s best attack utility was obtained,so as to calculate the best defensive utility of the defender.In order to maximize the effectiveness of network security defense,the optimal defense strategy under limited resources was implemented.Simulation experiments verify the effectiveness of the proposed method and analyze the influence of different parameter settings on the selection of defense strategy.     

博弈模型在传感器网络安全中的应用

入侵检测是传感器网络安全的重要研究内容,论文基于博弈论中的非合作模型提出了一种新型传感器网络入侵检测方案。该方案用一种只有两个参与者(攻击者和传感器网络)的非零非合作博弈模型来描述传感器网络中入侵检测问题,并证明了这个博弈模型可以达到纳什均衡,据此可以制定一个防御策略有效地提高入侵被检测到的概率。模拟试验证明这一模型是有效可行的。     

Defense decision-making method based on incomplete information stochastic game and Q-learning

Most of the existing stochastic games are based on the assumption of complete information,which are not consistent with the fact of network attack and defense.Aiming at this problem,the uncertainty of the attacker’s revenue was transformed to the uncertainty of the attacker type,and then a stochastic game model with incomplete information was constructed.The probability of network state transition is difficult to determine,which makes it impossible to determine the parameter needed to solve the equilibrium.Aiming at this problem,the Q-learning was introduced into stochastic game,which allowed defender to get the relevant parameter by learning in network attack and defense and to solve Bayesian Nash equilibrium.Based on the above,a defense decision algorithm that could learn online was designed.The simulation experiment proves the effectiveness of the proposed method.     

基于非合作博弈的无线自组织网络流量控制模型

该文根据无线自组织网络中流量控制和无线信道的特性,利用非合作博弈理论构造了基于网络流量速率和时延为参数的流量效用函数,建立了非合作博弈的无线自组织网络流量控制模型,证明了流量控制模型的Nash均衡解存在性,给出了模型的Nash均衡解的具体形式。数值仿真结果表明该模型存在Nash均衡解,能有效对网络中流量进行控制,满足不同业务的用户流量QoS需求。     

基于博弈论的跳频通信装备电子防御效能评估

为解决不同电子进攻条件下跳频通信装备电子防御效能动态评估问题,提出了一种基于博弈论的评估方法.首先,从技术性能、战术策略、操作人员3个角度构建评估指标体系;其次,通过4种归一化方法、复合权重、两种聚合模型,设计了一种多指标综合算法对通信电子防御效能进行评估;最后,将评估结果作为盈利,通过攻防双方博弈得到纳什均衡评估值.仿真分析表明,该方法能合理地考虑攻防双方、战术策略和操作人员对效能发挥的影响,与传统的静态评估方法相比更具科学性、优越性,具有一定的实用价值.     

Honeypot game‐theoretical model for defending against APT attacks with limited resources in cyber‐physical systems

A cyber‐physical system (CPS) is a new mechanism controlled or monitored by computer algorithms that intertwine physical and software components. Advanced persistent threats (APTs) represent stealthy, powerful, and well‐funded attacks against CPSs; they integrate physical processes and have recently become an active research area. Existing offensive and defensive processes for APTs in CPSs are usually modeled by incomplete information game theory. However, honeypots, which are effective security vulnerability defense mechanisms, have not been widely adopted or modeled for defense against APT attacks in CPSs. In this study, a honeypot game‐theoretical model considering both low‐ and high‐interaction modes is used to investigate the offensive and defensive interactions, so that defensive strategies against APTs can be optimized. In this model, human analysis and honeypot allocation costs are introduced as limited resources. We prove the existence of Bayesian Nash equilibrium strategies and obtain the optimal defensive strategy under limited resources. Finally, numerical simulations demonstrate that the proposed method is effective in obtaining the optimal defensive effect.     

Markov game modeling of mimic defense and defense strategy determination

Network mimic defense technology enhances the robustness of active defense through the redundancy,dynamic and diversity as well as the decision feedback mechanism.However,little work has been done for its security assessment and existing classic game models are not suitable for its dynamic characteristics and lack of universality.A Markov game model was proposed to analyze the transfer relationship between offensive and defensive status and the measurement method of safety and reliability of mimic defense,and calculated the offensive and defensive game equilibrium through non-linear programming algorithm to determine the best defensive strategy considering performance.Experiments give a comparison with the multi-target hiding technique and shows that the mimic defense has a higher defensive effect.Combining with the specific network case,the specific attack and defense path for the exploit of the system vulnerability is given and the effectiveness of the defense strategy algorithm is verified.     

基于系统动力学的网络安全攻防演化博弈模型简

基于非合作演化博弈理论,提出了在攻防双方信息不对称情况下具有学习机制的攻防演化博弈模型。结合攻防效用函数,对非合作演化博弈攻防过程中的纳什均衡点的存在性和唯一性进行论证。用系统动力学建立演化博弈模型进行仿真,仿真结果表明引入第三方动态惩罚策略的演化博弈模型存在纳什均衡,指出在网络安全技术进步的同时,发展攻击者追踪技术,增强网络攻击行为可审查性,实现动态惩罚,是解决网络安全问题的重要途径。     

Competitive flow control in general multi-node multi-link communication networks

In this paper, we consider the flow control in a general multi-node multi-link communication network with competing users. Each user has a source node, a destination node, and an existing route for its data flow over any set of links in the network from its source to its destination node. The flow rate for each user is a control variable that is determined by optimizing a user-specific utility function which combines maximizing the flow rate and minimizing the network congestion for that user. A preference parameter in the utility function allows each user to adjust the trade-off between these two objectives. Since all users share the same network resources and are only interested in optimizing their own utility functions, the Nash equilibrium of game theory represents a reasonable solution concept for this multi-user general network. The existence and uniqueness of such an equilibrium is therefore very important for the network to admit an enforceable flow configuration. In this paper, we derive an expression for the Nash equilibrium and prove its uniqueness. We illustrate the results with an example and discuss some properties and observations related to the network performance when in the Nash equilibrium. Copyright © 2007 John Wiley & Sons, Ltd.