无线传感器网络中虚假数据过滤方法
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摘要
无线传感器网络(WSN, Wireless sensor network)是当前信息领域的研究热点之一,具有广泛的应用前景。在WSN中,节点具有数量庞大、能量极其有限、计算和通信能力弱、存储空间低等特点。此外,由于传感器节点通常部署在野外或敌对环境中,因此易被攻击者所俘获。而节点通常不具备反篡改(tamper-resistant)(?)能力,在被妥协后将导致存储的密钥等秘密信息被泄露。
攻击者可以利用妥协节点向WSN中注入虚假数据、恶意篡改传输中的合法数据、或者发送重复数据等,这不仅会引发错误警报、干扰用户决策,还会耗尽宝贵的网络资源。本文旨在寻找能有效检测并过滤WSN中虚假数据的方法,主要研究工作包括：
(1)针对当前算法过滤概率低、节点开销不均衡的问题,提出了一种基于簇组织的虚假数据过滤方案CFFS。首先将节点组织成簇,并将簇头构建成一棵以Sink为根节点的树。然后提出一种基于节点负荷的分布式密钥分发算法,将源簇密钥分配给距离较近的转发簇进行存储,使得假包传输较少的跳数即可被过滤。理论分析及仿真实验表明,与已有基于对称密钥技术的算法相比,CFFS算法能显著提高虚假数据过滤概率,并有效均衡网络中节点的计算和通信开销。
(2)针对当前算法无法防范协同攻击的问题,提出了一种基于地理位置的虚假数据过滤方案GFFS。首先,各节点利用GPS获取地理位置并将位置信息预分发给部分其它节点存储。每个数据报告必须包含t个具有不同密钥分区的检测节点所产生的MAC及其地理位置。在转发过程中,中间节点通过验证数据包中MAC和地理位置的正确性以过滤虚假数据,还通过地理位置的合法性验证来检测由不同地理区域的多个妥协节点协同伪造的假包。理论分析及仿真实验表明,GFFS能有效防范协同攻击,且具备远强于已有方案的妥协容忍能力。例如当10个节点被妥协后,攻击者攻破SEF和GFFS的概率分别为93.2%,3%。
(3)考虑到GFFS需要昂贵的GPS等定位装置支持,进一步提出了一种基于邻居信息的虚假数据过滤方案NFFS。首先将每个节点的邻居信息预分发给部分转发节点存储,并在数据包中附带t个检测节点的ID以及所产生的MAC进行发送。接下来由转发节点对数据包中MAC的正确性以及各检测节点之间相对位置关系的合法性进行验证。理论分析及仿真实验表明,NFFS能有效防范协同攻击,并具备强于GFFS的妥协容忍能力。例如当10个节点被妥协后,攻击者攻破NFFS的概率仅为0.7%。
(4)针对当前方案仅能单独过滤虚假数据而无法过滤重复数据,或者由Sink检测重复数据而无法在转发过程中过滤的问题,提出了一种基于邻居信息和单向哈希链的过滤方案TNHFS。首先,各节点基于预置的随机数和单向函数生成一条单向哈希链,并将初始哈希值预分发给部分中间节点存储。检测到突发事件后,各感知节点将新鲜的哈希值和MAC附在数据包后面进行发送。在转发过程中,中间节点同时对数据包中包含的检测节点之间相对位置关系的合法性、MAC和哈希值的正确性以及哈希值的新鲜性进行验证。理论分析及仿真实验表明,NHFS能有效过滤传感器网络中的虚假数据和重复数据。
综上所述,本文对WSN中虚假数据过滤的问题进行了深入的研究,提出了多个性能更好的协议,具有较高的理论及应用价值。
Wireless sensor network (WSN) is one of the hot-spots in current research, and it holds a broad application foreground. In WSN, the nodes exhibit characteristics such as low power, weak communication and computational ability, limited storage space, etc. WSN is often deployed in unattended or even hostile environments, thus the nodes are easy to be compromised by the adversary. As nodes are often not equipped with tamper-resistant devices, and thus the keys stored in them will be disclosed when being compromised.
The adversary may abuse the compromised nodes to inject false data reports, tamper the transmitted legitimate reports, or send replayed reports into the network, which may cause not only false alarms, but also the depletion of the limited energy. In this thesis we focus on the problem that how to detect and filter out false reports in wireless sensor networks. The main research aspects are as follows.
(1) Aiming at the problems of existing schemes' low filtering probability and unbalanced overhead among nodes, we propose a cluster-based false data filtering scheme CFFS. First, nodes are grouped into clusters and a Sink-rooted tree of all cluster heads is constructed. We further propose a distributed key assignment method based on the overhead of nodes, which guarantees that the clusters closer to a source cluster hold more authentication keys for the source cluster than those clusters farther from it do. As a result, false reports generated by the source cluster can be filtered out within little hops. Analysis and simulation results show that CFFS outperforms existing schemes in filtering efficiency, and achieves more balanced key distribution.
(2) Aiming at the problem that existing schemes cannot resist the collaborative false data injection attacks, we propose a Geographical information based False reports Filtering Scheme (GFFS) in sensor networks. In GFFS, each node distributes its location information to some downstream nodes after being deployed. Each data report must carry the MACs and locations of t detecting nodes that sensed the event simultaneously. All forwarding nodes then validate not only the correctness of the MACs and locations, but also the legitimacy of the locations. As a result, false reports injected collaboratively by compromised nodes from different geographical areas can be detected and filtered out. For exampe, with ten compromised nodes, the probability for the attacker to break down SEF and GFFS is93.2%,3%, respectively.
(3) Considering that GFFS requires the support of expensive positioning devices, we further propose a NFFS scheme. In NFFS, each node distributes its neighbor information to some other nodes after deployment. When a report is generated for an observed event, it must carry the IDs and MACs from t detecting nodes. Each forwarding node checks not only the correctness of the MACs carried in the report, but also the legitimacy of the relative position of these detecting nodes. As a result, collaborative false data injection attacks can be resisted efficiently. For example, with ten compromised nodes, there is a probability of only0.7%for the attacker to break down NFFS.
(4) Aiming at the problem that existing schemes can detect and filter out only false reports but the replayed reports during forwarding, or can only detect replayed reports by the Sink but not the forwarding nodes, we propose a Neighbor information and one-way Hash chain based Filtering Scheme (NHFS). In NHFS, each node distributes its initial hash value to some other nodes after deployment. When a report is generated for an observed event, it must carry the MACs and fresh hash values from t detecting nodes. Each forwarding node checks the correctness of the MACs and hash values, and the freshness of these hash values. Analysis and simulation results show that NHFS can detect and filter out false reports and replayed reports efficiently.
In a word, the dissertation performs an in-depth study on the filtering of false data reports in wireless sensor networks. Moreover, several filtering protocols with higher performance are proposed. Therefore, the research has strong theoretical and practical significances.
攻击者可以利用妥协节点向WSN中注入虚假数据、恶意篡改传输中的合法数据、或者发送重复数据等,这不仅会引发错误警报、干扰用户决策,还会耗尽宝贵的网络资源。本文旨在寻找能有效检测并过滤WSN中虚假数据的方法,主要研究工作包括：
(1)针对当前算法过滤概率低、节点开销不均衡的问题,提出了一种基于簇组织的虚假数据过滤方案CFFS。首先将节点组织成簇,并将簇头构建成一棵以Sink为根节点的树。然后提出一种基于节点负荷的分布式密钥分发算法,将源簇密钥分配给距离较近的转发簇进行存储,使得假包传输较少的跳数即可被过滤。理论分析及仿真实验表明,与已有基于对称密钥技术的算法相比,CFFS算法能显著提高虚假数据过滤概率,并有效均衡网络中节点的计算和通信开销。
(2)针对当前算法无法防范协同攻击的问题,提出了一种基于地理位置的虚假数据过滤方案GFFS。首先,各节点利用GPS获取地理位置并将位置信息预分发给部分其它节点存储。每个数据报告必须包含t个具有不同密钥分区的检测节点所产生的MAC及其地理位置。在转发过程中,中间节点通过验证数据包中MAC和地理位置的正确性以过滤虚假数据,还通过地理位置的合法性验证来检测由不同地理区域的多个妥协节点协同伪造的假包。理论分析及仿真实验表明,GFFS能有效防范协同攻击,且具备远强于已有方案的妥协容忍能力。例如当10个节点被妥协后,攻击者攻破SEF和GFFS的概率分别为93.2%,3%。
(3)考虑到GFFS需要昂贵的GPS等定位装置支持,进一步提出了一种基于邻居信息的虚假数据过滤方案NFFS。首先将每个节点的邻居信息预分发给部分转发节点存储,并在数据包中附带t个检测节点的ID以及所产生的MAC进行发送。接下来由转发节点对数据包中MAC的正确性以及各检测节点之间相对位置关系的合法性进行验证。理论分析及仿真实验表明,NFFS能有效防范协同攻击,并具备强于GFFS的妥协容忍能力。例如当10个节点被妥协后,攻击者攻破NFFS的概率仅为0.7%。
(4)针对当前方案仅能单独过滤虚假数据而无法过滤重复数据,或者由Sink检测重复数据而无法在转发过程中过滤的问题,提出了一种基于邻居信息和单向哈希链的过滤方案TNHFS。首先,各节点基于预置的随机数和单向函数生成一条单向哈希链,并将初始哈希值预分发给部分中间节点存储。检测到突发事件后,各感知节点将新鲜的哈希值和MAC附在数据包后面进行发送。在转发过程中,中间节点同时对数据包中包含的检测节点之间相对位置关系的合法性、MAC和哈希值的正确性以及哈希值的新鲜性进行验证。理论分析及仿真实验表明,NHFS能有效过滤传感器网络中的虚假数据和重复数据。
综上所述,本文对WSN中虚假数据过滤的问题进行了深入的研究,提出了多个性能更好的协议,具有较高的理论及应用价值。
Wireless sensor network (WSN) is one of the hot-spots in current research, and it holds a broad application foreground. In WSN, the nodes exhibit characteristics such as low power, weak communication and computational ability, limited storage space, etc. WSN is often deployed in unattended or even hostile environments, thus the nodes are easy to be compromised by the adversary. As nodes are often not equipped with tamper-resistant devices, and thus the keys stored in them will be disclosed when being compromised.
The adversary may abuse the compromised nodes to inject false data reports, tamper the transmitted legitimate reports, or send replayed reports into the network, which may cause not only false alarms, but also the depletion of the limited energy. In this thesis we focus on the problem that how to detect and filter out false reports in wireless sensor networks. The main research aspects are as follows.
(1) Aiming at the problems of existing schemes' low filtering probability and unbalanced overhead among nodes, we propose a cluster-based false data filtering scheme CFFS. First, nodes are grouped into clusters and a Sink-rooted tree of all cluster heads is constructed. We further propose a distributed key assignment method based on the overhead of nodes, which guarantees that the clusters closer to a source cluster hold more authentication keys for the source cluster than those clusters farther from it do. As a result, false reports generated by the source cluster can be filtered out within little hops. Analysis and simulation results show that CFFS outperforms existing schemes in filtering efficiency, and achieves more balanced key distribution.
(2) Aiming at the problem that existing schemes cannot resist the collaborative false data injection attacks, we propose a Geographical information based False reports Filtering Scheme (GFFS) in sensor networks. In GFFS, each node distributes its location information to some downstream nodes after being deployed. Each data report must carry the MACs and locations of t detecting nodes that sensed the event simultaneously. All forwarding nodes then validate not only the correctness of the MACs and locations, but also the legitimacy of the locations. As a result, false reports injected collaboratively by compromised nodes from different geographical areas can be detected and filtered out. For exampe, with ten compromised nodes, the probability for the attacker to break down SEF and GFFS is93.2%,3%, respectively.
(3) Considering that GFFS requires the support of expensive positioning devices, we further propose a NFFS scheme. In NFFS, each node distributes its neighbor information to some other nodes after deployment. When a report is generated for an observed event, it must carry the IDs and MACs from t detecting nodes. Each forwarding node checks not only the correctness of the MACs carried in the report, but also the legitimacy of the relative position of these detecting nodes. As a result, collaborative false data injection attacks can be resisted efficiently. For example, with ten compromised nodes, there is a probability of only0.7%for the attacker to break down NFFS.
(4) Aiming at the problem that existing schemes can detect and filter out only false reports but the replayed reports during forwarding, or can only detect replayed reports by the Sink but not the forwarding nodes, we propose a Neighbor information and one-way Hash chain based Filtering Scheme (NHFS). In NHFS, each node distributes its initial hash value to some other nodes after deployment. When a report is generated for an observed event, it must carry the MACs and fresh hash values from t detecting nodes. Each forwarding node checks the correctness of the MACs and hash values, and the freshness of these hash values. Analysis and simulation results show that NHFS can detect and filter out false reports and replayed reports efficiently.
In a word, the dissertation performs an in-depth study on the filtering of false data reports in wireless sensor networks. Moreover, several filtering protocols with higher performance are proposed. Therefore, the research has strong theoretical and practical significances.
引文
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