一种基于云模型的WSNs节点信誉安全方案

针对已有基于轻量云模型的节点信誉安全方案中存在的决策困难问题以及推荐节点的恶意行为识别问题,提出了优化的轻量云模型MLCM(Modified Light-weighted Cloud Model),并在此基础上设计实现一种新型的节点信誉安全方案.首先,在传统无线传感网信任管理信任值的处理方式的基础上,对节点的直接信任值和间接信任值进行综合处理后再利用云模型简化逆向云算子进行计算,以解决信任误判问题;其次,用云隶属度函数计算推荐信任值,在涉及推荐节点信任值计算时可以提高恶意节点识别的准确度.实验表明,该方案在克服传统的入侵容忍和敏感度之间矛盾问题的同时,还解决了攻击节点对单一节点发动攻击时造成的决策困难问题和恶意节点准确识别问题.     

SDN网络中基于信誉机制的恶意节点识别系统设计

恶意节点会影响SDN网络的稳定运行,因此,设计有效的恶意节点识别系统具有重要意义。针对SDN网络提出基于信誉机制的恶意节点识别系统。首先,通过设计控制中心模块、监控模块、响应模块和检测模块组建系统整体结构;然后,在云模型的基础上设计节点信誉机制,获取节点在SDN网络中的信任值,根据信任值识别恶意节点;最后,设计了信任更新机制,跟踪节点在不同时刻的信任值变化情况,提高对恶意节点的识别精度。实验结果表明,该系统具有较高的恶意节点识别精度和识别率。     

主动撤销密钥的密钥管理方案

在动态密钥管理系统中,频繁的控制信息能提高无线传感器网络(WSN)的健壮性,但也增大了WSN能量消耗。当网络中有节点处于睡眠状态时,问题更严重。在传感器节点睡眠状态时,恶意节点可能利用合法节点的ID哄骗进入传感器网络。在传感器节点睡眠状态时,合法节点可能被捕获。文章首先介绍安全密钥产生方案,然后介绍睡眠验证协议,目的是防止哄骗睡眠节点,最后介绍主动防御机制,防御恶意节点进入传感器网络。     

移动无线传感网恶意数据包传播随机模型

该文基于随机元胞自动机理论研究移动无线传感器网络中恶意数据包的传播行为.依据移动无线传感器网络的信息扩散机制,设计节点移动模型、无线信道分配算法、信号干扰模型和恶意数据包传播模型,定义移动传感器网络环境下的元胞空间、元胞邻域、元胞状态以及状态转换规则,提出移动无线传感器网络中恶意数据包传播的随机元胞自动机模型,研究在不确定环境下恶意数据包传播的时空特征.研究结果表明该模型能够有效预测恶意数据包传播的趋势和空间分布状态,有效克服基于马尔科夫随机场理论、常微分方程或差分方程建立的恶意数据包传播模型而无法描述恶意数据包传播的空间分布状态的不足.     

融合信誉评估与巡查机制的WSN能量高效安全成簇算法

无线传感网（Wireless Sensor Network,WSN）因节点众多、通信开放、资源有限,且通常部署在无人值守区域,表现出明显的安全脆弱性。提出了基于具有能量高效特征巡查机制的WSN安全应对模式,设计出了一种融合信誉评估与巡查机制的WSN安全成簇算法：首先构建簇化层次式网络模型,基于节点的剩余能量和簇质心距离选取簇头节点,提升节点能量消耗的均衡性;并建立节点信誉评估模型,通过巡查机制随机检测节点的通信行为,基于节点行为对信息安全的威胁程度,利用指数函数计算并及时更新节点的信誉值,放大性地惩罚节点的异常行为,有助于节能和快速发现恶意节点。仿真测试结果表明,算法能够均衡传感器节点的能量消耗,在不明显缩短网络生命周期的情况下,对无线传感网中恶意节点具有较高的检测速率和97%的检测率。     

P2P电子商务环境下的动态安全信任管理模型

针对P2P电子商务中的信任评估问题,提出一种基于信任云的动态安全信任管理模型(TCDSTM).TCDSTM利用云理论来刻画信任及信任等级,然后给出具有抗攻击能力的全局信任度融合算法、节点的类型识别和拓扑重构机制.理论分析和仿真实验一致表明,TCDSTM小仅可以抵御共谋、振荡等恶意攻击,而且大大提高了交易成功率.此外,该模型具有较低的通信丌销.     

WSN入侵检测系统ARMAN测模型分析研究

无线传感网络(Wireless Sensor Networks,WSN)作为新兴的测控网络技术,是能够自主实现数据采集、融合和传输应用的智能网络应用系统.WSN因其巨大优势和广阔的应用前景引起各界的关注.开放的分布式架构给无线传感器网络的安全带来了很大的挑战.而安全是无线传感器网络可用的前提,针对这一问题,迫切需要设计出能有效检测出网络入侵攻击的入侵检测系统.文章对无线传感网络安全特性进行了深八分析和研究,提出一种基于流量的ARMA预测模型的入侵检测技术.该模型利用传感器节点信息的关系和特点,增加了网络的安全性,使得网络能灵活的适应无线传感器网络拓扑结构的变化.     

无线传感器网络的神经网络模型

无线传感器网络是复杂的无线网络。无线传感器网络拥有大量的网络节点。网络节点是无线传感器网络的基础。为了研究复杂的无线传感器网络，采用了神经元描述了WSN的网络节点，用神经元模型表示了无线传感器网络。给出了无线待感器网络节点的神经元模型和无线传感器网络的神经网络模型，并将神经网络应用于无线传感器网络的数据融合应用。结果表明，基于神经网络的无线传感器网络研究可以使得复杂研究变得简单，利于开展WSN的深入研究。     

一种基于云模型的WSN鲁棒评价机制

针对WSN(无线传感器网络)鲁棒性评估中存在的模糊性和随机性问题,引入云模型对无线传感器网络系统状态进行描述和检测,提出了基于云模型的无线传感器网络系统鲁棒性的评估与预测模型.最后通过试验与分析,进一步说明了本论文中模型的合理性,为当前无线传感器网络鲁棒性的研究提供了一种有价值的新思路.     

基于信任度的WSN信任模型研究

针对无线传感器网络面临的内部攻击,结合现有经典的信任机制,提出一种基于信任度的信任模型。模型由信任度计算、信任管理及信任决策3层组成。以信任度计算为核心,将直接信任度、间接信任度以及能量信任度合成综合信任度。采用周期更新信任度的方式,降低网络能量消耗。设定信任度阈值进行信任决策,高效地辨别出恶意节点和老化节点,保证网络的安全性和可靠性。仿真实验表明,该模型能够有效检测恶意节点,具有较高的检测率,同时很大程度上降低了整个网络的能量消耗。     

基于Beta分布的无线传感器网络分簇路由信任管理机制研究

信任管理机制为保障无线传感器网络安全提供了一种有效方案,通过对节点的行为进行评价,建立整个网络的信任管理机制,对判断的恶意节点采用相应的限制措施来保障安全。文章以Beta概率密度分布函数的期望值作为信任值,优化网络分簇路由协议,可在保障网络能量高效利用的同时,有效提升整个网络的安全性。     

An efficient weighted trust‐based malicious node detection scheme for wireless sensor networks

The aim of wireless sensor networks (WSNs) is to gather sensor data from a monitored environment. However, the collected or reported information might be falsified by faults or malicious nodes. Hence, identifying malicious nodes in an effective and timely manner is essential for the network to function properly and reliably. Maliciously behaving nodes are usually detected and isolated by reputation and trust‐based schemes before they can damage the network. In this paper, we propose an efficient weighted trust‐based malicious node detection (WT‐MND) scheme that can detect malicious nodes in a clustered WSN. The node behaviors are realistically treated by accounting for false‐positive and false‐negative instances. The simulation results confirm the timely identification and isolation of maliciously behaving nodes by the WT‐MND scheme. The effectiveness of the proposed scheme is afforded by the adaptive trust‐update process, which implicitly performs trust recovery of temporarily malfunctioning nodes and computes a different trust‐update factor for each node depending on its behavior. The proposed scheme is more effective and scalable than the related schemes in the literature, as evidenced by its higher detection ratio (DR) and lower misdetection ratio (MDR), which only slightly vary with the network's size. Moreover, the scheme sustains its efficient characteristics without significant power consumption overheads.     

A Risk-Aware Reputation-Based Trust Management in Wireless Sensor Networks

Wireless sensor networks (WSNs) are susceptible to many security threats and are specifically prone to physical node capture in which the adversary can easily launch the so-called insider attacks such as node compromise, bypassing the traditional security mechanisms based on cryptography primitives. So, the compromised nodes can be modified to misbehave and disrupt the entire network and can successfully perform the authentication process with their neighbors, which have no way to distinguish fraudulent nodes from trustworthy ones. Trust and reputation systems have been recently suggested as a powerful tools and an attractive complement to cryptography-based schemes in securing WSNs. They provide ability to detect and isolate both faulty and malicious nodes. Considerable research has been done on modeling and managing trust and reputation. However, trust topic issue in WSNs remains an open and challenging field. In this paper, we propose a Risk-aware Reputation-based Trust (RaRTrust) model for WSNs. Our novel framework uses both reputation and risk to evaluate trustworthiness of a sensor node. Risk evaluation is used to deal with the dramatic spoiling of nodes, which makes RaRTrust robust to on–off attack and differ from other trust models based only on reputation. This paper contributes to model the risk as opinion of short-term trustworthiness combining with traditional reputation evaluation to derive trustworthiness in WSNs.

     

基于ECC和ID签名的WSN广播快速认证方案

广播认证是无线传感器网络(WSN)的一种基本安全服务,针对现有认证方案的计算量大、认证速度慢等问题,提出一种基于椭圆曲线加密(ECC)和身份(ID)签名的WSN广播快速认证方案.对现有EIBAS签名认证方案进行改进,通过节点间的合作,共享中间计算结果来减少邻居节点的计算量,以此提高认证速度,减少能量消耗.同时,提出一种安全机制,通过对多个邻居共享数据的对比来抵御恶意节点的攻击.实验结果表明,该方案相对于传统的椭圆曲线加密算法能够提高约42％的签名认证速度,降低约36％的能耗,大大延长网络生命周期.     

基于多因素信任的分布式信任模型

提出了一种基于多因素信任的分布式信任模型,通过引用时效函数来区分不同时间内的信任在直接信任度中的重要性。在计算节点的综合信任值时,通过对节点剩余能量的分析,加入了能量信任值,并通过将通信信任和能量信任结合在一起,适当地调节通信信任和能量信任的权重大小来评价传感器节点的可信度。仿真实验与分析证明,该模型较其他模型,能更快的识别出恶意节点,在节点交互成功率和网络的安全性方面均有大幅提高。     

无线网状网中基于D-S证据理论的可信路由

结合Dempster-Shafer(D-S)证据理论,提出了一个信任评估模型;同时,在AODV的基础上,给出了一个可信的路由协议T-AODV,该路由协议根据节点的信任值选择可信的路由进行数据传输。仿真结果表明,所提机制能够有效地监测和隔离恶意节点,抵御攻击,能够提高网络的可靠性、顽健性以及安全性。     

基于信任关系的IP网络容错容侵机制

目前IP网络受自身故障和网络攻击等异常行为影响较过去更深广。因此如何增强IP网络的容错和容侵能力显得尤为重要。但是目前很多的研究仅关注其中一个方面,而很少两者兼有,从而不能很好地兼顾安全性和可生存性。该文提出了一种有效的基于信任关系的容错容侵机制。该机制借用了社会网络中的信任关系思想,定量地描述了信任关系值与网络行为的对应关系某节点的恶意行为会使得自己在其他节点处的信任值下降。然后,分析了该机制如何对3种网络异常,即自身故障、诋毁攻击和矛盾行为攻击的容忍能力。最后,仿真结果和分析表明该机制可以迅速而精确地检测到异常节点,并能有效地阻止这些异常对网络的攻击和破坏。     

A Hybrid Trust Management Scheme for Wireless Sensor Networks

Wireless sensor network (WSN) consists of wireless small sensor nodes deployed in the terrain for continuous observation of physical or environmental conditions. The data collected from the WSN is used for making decisions. The condition for making critical decision is to assure the trustworthiness of the data generated from sensor nodes. However, the approaches for scoring the sensed data alone is not enough in WSN since there is an interdependency between node and data item. If the overall trust score of the network is based on one trust component, then the network might be misguided. In this work, we propose the hybrid approach to address the issue by assigning the trust score to data items and sensor nodes based on data quality and communication trust respectively. The proposed hybrid trust management scheme (HTMS) detects the data fault with the help of temporal and spatial correlations. The correlation metric and provenance data are used to score the sensed data. The data trust score is utilized for making decision. The communication trust and provenance data are used to evaluate the trust score of intermediate nodes and source node. If the data item is reliable enough to make critical decisions, a reward is given by means of adding trust score to the intermediate nodes and source node. A punishment is given by reducing the trust score of the source and intermediate nodes, if the data item is not reliable enough to make critical decisions. Result shows that the proposed HTMS detects the malicious, faulty, selfish node and untrustworthy data.     

A Cognitive Energy Efficient and Trusted Routing Model for the Security of Wireless Sensor Networks: CEMT

There are many smart applications evolved in the area of the wireless sensor networks. The applications of WSNs are exponentially increasing every year which creates a lot of security challenges that need to be addressed to safeguard the devices in WSN. Due to the dynamic characteristics of these resource constrained devices in WSN, there must be high level security requirements to be considered to create a high secure environments. This paper presents an efficient multi attribute based routing algorithm to provide secure routing of information for WSNs. The work proposed in this paper can decrease the energy and enhances the performance of the network than the currently available routing algorithm such as multi-attribute pheromone ant secure routing algorithm based on reputation value and ant-colony optimization algorithm. The proposed work secures the network environment with the improved detection techniques based on nodes’ higher coincidence rates to find the malicious behavior using trust calculation algorithm. This algorithm uses some QoS parameters such as reliability rate, elapsed time to detect impersonation attacks, and stability rate for trust related attacks, to perform an efficient trust calculation of the nodes in communication. The outcome of the simulation show that the proposed method enhances the performance of the network with the improved detection rate and secure routing service.