无线网络攻击建模与检测关键技术研究
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摘要
无线网络接入媒质的开放性带来很多安全方面的问题。对无线网络的安全问题研究主要集中在物理层、MAC层和网络层,内容包括：攻击方法的定义,攻击模型的建立,检测及防御措施等方面。如果能够对各种攻击建立准确的数学分析模型,以定量的方式对无线网络在受到各种攻击时的网络性能进行分析,无疑对网络对抗的研究是非常有益的。虽然有一些文献根据对抗手段建立了分析模型,但模型的准确性和实用性都不够。
本论文的主要研究内容是建立无线网络在受到攻击时的分析模型和检测模型,试图从概率的角度分析无线网络在各种攻击下的性能变化,通过对网络外部可观测量的变换,提取表征网络受到攻击时的特征参量,建立对攻击行为的检测模型,估计攻击参数,这将对无线网络安全及应对措施的研究起到技术支撑作用。此外还对安全路由协议的攻击方法以及恶意节点隔离方法进行了研究。本论文的主要工作和创新点如下：
(1)研究了随机干扰对无线网络性能的影响,以及对其进行检测的机制。(ⅰ)建立了存在干扰机时的IEEE802.11DCF无线网络二维马尔可夫性能分析模型；(ⅱ)给出了一个全新的无线网络干扰检测模型,通过测量无线网络信道连续空闲时隙数,估计出当前网络节点的数据包发送概率,并推测网络在没有受到干扰时理论上的成功发送概率,与实际测量结果比较来实现对干扰的检测；(ⅲ)通过NS2网络模拟对网络分析模型和干扰检测模型进行验证。
(2)研究了智能干扰对无线网络性能的影响,以及对干扰进行检测的机制。(ⅰ)建立了基于MAC协议信息的智能干扰信道状态模式转换模型；(ⅱ)提出了基于对无线网络信道状态转换模式进行统计的方法来检测智能干扰的检测模型,并对智能干扰概率进行估计；(ⅲ)通过NS2网络模拟进行验证。
(3)研究了采用回退窗口缩小的自私行为攻击,(ⅰ)对存在回退窗口缩小行为自私节点的无线网络建立了马尔可夫分析模型,得到自私节点和正常节点的数据包发送冲突概率,并利用模型得到的冲突概率计算自私节点在相邻两次成功发送之间的回退次数概率分布；(ⅱ)提出了回退次数主要分布在小于所选定的回退窗口值的范围内,因此对自私行为的检测通过在对回退次数小于标准规定的0-32之间的单个节点的分布与平均分布进行累积差值比较,超过门限即可判决自私节点的存在；(ⅲ)根据节点在相邻成功发送之间经历的回退次数概率密度函数的阶跃性,提出了对自私回退窗口参数估计方法；(ⅳ)通过NS2网络模拟对分析模型和检测模型进行验证。
(4)研究了NAV扩展自私行为和DIFS缩小自私行为。(ⅰ)为NAV扩展自私行为建立了性能分析模型；(ⅱ)分析了自私节点因采用DIFS缩小行为而获得的回退次数减少的量化结果；(ⅲ)提出了通过统计网络中节点在信道开始空闲后不同时刻开始发送的概率,进行NAV扩展自私行为检测,并对NAV扩展参数进行估计；(ⅳ)给出了DIFS缩小行为的检测方法和参数估计方法；当网络节点选择回退值为0时,从网络信道空闲开始到信道占用之间的时间间隔就是该节点选择的DIFS时间,根据该值与标准规定是否偏离来检测DIFS缩小自私行为的存在,并估计所选择的缩小参数；(ⅴ)用NS2网络模拟的方法进行了验证。
(5)提出了一种针对安全路由协议SAODV的抢先重放攻击方法；通过将受恶意节点影响的节点状态设置为非活动状态,来逐步实现对恶意节点隔离；证明了最多通过五次路由建立过程就能够对恶意节点完全隔离；处于非活动状态的节点不能够作为中继节点进行数据转发,但能够作为源节点或目的节点进行网络通信。
The opening character of wireless access medium lead many security issues. Research of wireless security focus on physical layer, MAC layer and network layer, and some of key points include:the definition of attack method, establishment of attack model, detection and defense mechanisms against attack. It's instructive to the research if we can establish an accurate model about various attack and quantitative analysis the performance of network. Although some analysis model have been proposed, the veracity and practicability of these models are not perfect.
The purpose of the thesis is to establish theoretic model and analysis attack effects in the view of probability about wireless network under random jamming, intelligent jamming and selfish behavior. Character parameters that depict attack behavior can be extracted through transformation of measured parameter, and detecting model can be established, and this work will plays important role to support the research of countermeasures of wireless attack. Methods to achieve rushing attack of SAODV and to separate malicious node are proposed.Contributions and innovations of the thesis as below:
(1) Effects of random jamming on performance of wireless network and jamming detection methods are considered.(i) A MARKOV model with a jammer in the IEEE802.11DCF wireless network is established;(ii) a novel jamming detection model is proposed, of which through measure idle slots of wireless channel, packet sending probability of wireless node can be estimated, and the theoretic successful transmission probability that without jammer can be computed, and compare with the measured probability, the jammer can be detected;(iii) validation of the model is performed through NS2network simulator.
(2) Effects of intelligent jamming on performance of wireless network and jamming detection methods are considered.(i) A state transition model of wireless channel under intelligent jamming is established;(ii) detection method based on channel state transition is proposed, and the probability of jamming can be estimated;(iii) validation of the model is performed through NS2network simulator.
(3) Selfish behavior based on backoff window reduction is considered,(i) A MARKOV model on wireless network with backoff reduction selfish node is proposed, collision probabilities of selfish and normal node are computed, by which the probability of backoff slots between two successful transmission of the same node is obtained;(ii) The probability of backoff slots is mainly lies no more than backoff window, so compare the distribution of specific node to the average of network nodes that lies between0-32, the selfish behavior node can be detected,(iii) Based on the character that the curve of probability density of backoff slots appears steep, a method to estimate selfish parameter is proposed;(iv) validation is performed through NS2simulator.
(4) Selfish behaviors base on NAV extension and DIFS reduction are considered.(i) A performance analytic model on NAV extension behavior is established;(ii) Quantification result of backoff value advantage by DIFS reduction is analyzed;(iii) A NAV extension selfish behavior detection method is proposed, which by compute the probability of different time slots that from the channel become idle state to transmit state, and the NAV extension parameter is estimated;(iv) A detection method on DIFS reduction and a parameter estimation method are proposed; when the backoff value equals zero, the interval from channel idle to channel busy is the DIFS time, so selfish behavior can be detected by checking the interval;(v) validation on the analytic model, detection model and parameter estimation are performed through NS2simulation.
(5) A rushing replay attack on security routing protocol SAODV is proposed; The malicious node can be separated through set the affected node to inactive state; it is proved that the malicious node can be completely separated no more than five round; nodes that lies in the coverage of malicious node become inactive state, which cannot relay data, but can send data as a source node or receive data as a destination node.
本论文的主要研究内容是建立无线网络在受到攻击时的分析模型和检测模型,试图从概率的角度分析无线网络在各种攻击下的性能变化,通过对网络外部可观测量的变换,提取表征网络受到攻击时的特征参量,建立对攻击行为的检测模型,估计攻击参数,这将对无线网络安全及应对措施的研究起到技术支撑作用。此外还对安全路由协议的攻击方法以及恶意节点隔离方法进行了研究。本论文的主要工作和创新点如下：
(1)研究了随机干扰对无线网络性能的影响,以及对其进行检测的机制。(ⅰ)建立了存在干扰机时的IEEE802.11DCF无线网络二维马尔可夫性能分析模型；(ⅱ)给出了一个全新的无线网络干扰检测模型,通过测量无线网络信道连续空闲时隙数,估计出当前网络节点的数据包发送概率,并推测网络在没有受到干扰时理论上的成功发送概率,与实际测量结果比较来实现对干扰的检测；(ⅲ)通过NS2网络模拟对网络分析模型和干扰检测模型进行验证。
(2)研究了智能干扰对无线网络性能的影响,以及对干扰进行检测的机制。(ⅰ)建立了基于MAC协议信息的智能干扰信道状态模式转换模型；(ⅱ)提出了基于对无线网络信道状态转换模式进行统计的方法来检测智能干扰的检测模型,并对智能干扰概率进行估计；(ⅲ)通过NS2网络模拟进行验证。
(3)研究了采用回退窗口缩小的自私行为攻击,(ⅰ)对存在回退窗口缩小行为自私节点的无线网络建立了马尔可夫分析模型,得到自私节点和正常节点的数据包发送冲突概率,并利用模型得到的冲突概率计算自私节点在相邻两次成功发送之间的回退次数概率分布；(ⅱ)提出了回退次数主要分布在小于所选定的回退窗口值的范围内,因此对自私行为的检测通过在对回退次数小于标准规定的0-32之间的单个节点的分布与平均分布进行累积差值比较,超过门限即可判决自私节点的存在；(ⅲ)根据节点在相邻成功发送之间经历的回退次数概率密度函数的阶跃性,提出了对自私回退窗口参数估计方法；(ⅳ)通过NS2网络模拟对分析模型和检测模型进行验证。
(4)研究了NAV扩展自私行为和DIFS缩小自私行为。(ⅰ)为NAV扩展自私行为建立了性能分析模型；(ⅱ)分析了自私节点因采用DIFS缩小行为而获得的回退次数减少的量化结果；(ⅲ)提出了通过统计网络中节点在信道开始空闲后不同时刻开始发送的概率,进行NAV扩展自私行为检测,并对NAV扩展参数进行估计；(ⅳ)给出了DIFS缩小行为的检测方法和参数估计方法；当网络节点选择回退值为0时,从网络信道空闲开始到信道占用之间的时间间隔就是该节点选择的DIFS时间,根据该值与标准规定是否偏离来检测DIFS缩小自私行为的存在,并估计所选择的缩小参数；(ⅴ)用NS2网络模拟的方法进行了验证。
(5)提出了一种针对安全路由协议SAODV的抢先重放攻击方法；通过将受恶意节点影响的节点状态设置为非活动状态,来逐步实现对恶意节点隔离；证明了最多通过五次路由建立过程就能够对恶意节点完全隔离；处于非活动状态的节点不能够作为中继节点进行数据转发,但能够作为源节点或目的节点进行网络通信。
The opening character of wireless access medium lead many security issues. Research of wireless security focus on physical layer, MAC layer and network layer, and some of key points include:the definition of attack method, establishment of attack model, detection and defense mechanisms against attack. It's instructive to the research if we can establish an accurate model about various attack and quantitative analysis the performance of network. Although some analysis model have been proposed, the veracity and practicability of these models are not perfect.
The purpose of the thesis is to establish theoretic model and analysis attack effects in the view of probability about wireless network under random jamming, intelligent jamming and selfish behavior. Character parameters that depict attack behavior can be extracted through transformation of measured parameter, and detecting model can be established, and this work will plays important role to support the research of countermeasures of wireless attack. Methods to achieve rushing attack of SAODV and to separate malicious node are proposed.Contributions and innovations of the thesis as below:
(1) Effects of random jamming on performance of wireless network and jamming detection methods are considered.(i) A MARKOV model with a jammer in the IEEE802.11DCF wireless network is established;(ii) a novel jamming detection model is proposed, of which through measure idle slots of wireless channel, packet sending probability of wireless node can be estimated, and the theoretic successful transmission probability that without jammer can be computed, and compare with the measured probability, the jammer can be detected;(iii) validation of the model is performed through NS2network simulator.
(2) Effects of intelligent jamming on performance of wireless network and jamming detection methods are considered.(i) A state transition model of wireless channel under intelligent jamming is established;(ii) detection method based on channel state transition is proposed, and the probability of jamming can be estimated;(iii) validation of the model is performed through NS2network simulator.
(3) Selfish behavior based on backoff window reduction is considered,(i) A MARKOV model on wireless network with backoff reduction selfish node is proposed, collision probabilities of selfish and normal node are computed, by which the probability of backoff slots between two successful transmission of the same node is obtained;(ii) The probability of backoff slots is mainly lies no more than backoff window, so compare the distribution of specific node to the average of network nodes that lies between0-32, the selfish behavior node can be detected,(iii) Based on the character that the curve of probability density of backoff slots appears steep, a method to estimate selfish parameter is proposed;(iv) validation is performed through NS2simulator.
(4) Selfish behaviors base on NAV extension and DIFS reduction are considered.(i) A performance analytic model on NAV extension behavior is established;(ii) Quantification result of backoff value advantage by DIFS reduction is analyzed;(iii) A NAV extension selfish behavior detection method is proposed, which by compute the probability of different time slots that from the channel become idle state to transmit state, and the NAV extension parameter is estimated;(iv) A detection method on DIFS reduction and a parameter estimation method are proposed; when the backoff value equals zero, the interval from channel idle to channel busy is the DIFS time, so selfish behavior can be detected by checking the interval;(v) validation on the analytic model, detection model and parameter estimation are performed through NS2simulation.
(5) A rushing replay attack on security routing protocol SAODV is proposed; The malicious node can be separated through set the affected node to inactive state; it is proved that the malicious node can be completely separated no more than five round; nodes that lies in the coverage of malicious node become inactive state, which cannot relay data, but can send data as a source node or receive data as a destination node.
引文
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