无线传感器网络中欺骗攻击定位技术研究

发布时间：2018-04-04 22:22

本文选题：无线传感器网络　切入点：攻击检测　出处：《江南大学》2017年硕士论文

【摘要】：无线传感器网络本身的特性使其面临比传统有线和无线网络更多的安全威胁,欺骗攻击则是其安全领域必须攻克的难题之一。近些年来,国内外学者对攻击检测及攻击定位进行了研究,并获得了一些研究成果,但是无线传感器网络中的欺骗攻击研究仍然是一个挑战性的问题,有待进一步深入研究。因此,本文研究由欺骗攻击所衍生的一系列科学问题,如欺骗攻击检测、欺骗攻击源定位等,得到了一些新算法应用于无线传感器网络欺骗攻击定位技术中。本文的主要研究工作包括:(1)基于改进粒子群优化算法的K均值欺骗攻击检测模型的研究。KIPSO欺骗攻击检测模型将欺骗攻击检测描述为一个统计假设检验,基于接收信号强度与物理位置的相关性,利用不同位置节点接收信号强度的差异进行攻击检测。攻击检测阶段,首先使用KIPSO聚类算法对接收信号强度进行聚类分析,从而计算类中心之间的距离,最终通过阈值检测判断节点是否受到欺骗攻击。仿真结果表明,KIPSO欺骗攻击检测模型能在提高检出率、增强报警可信度的同时,有效解决K均值算法陷入局部极值的问题。(2)多目标二进制粒子群攻击定位任务分配算法的研究。多目标二进制粒子群攻击定位任务分配算法根据任务完成总时间、总能量消耗和负载平衡度来建立代价函数,在工作负荷和接收信号强度空间约束条件下,完成多目标优化的任务分配工作。MOBPSOTA算法中的惯性权重采取非线性的更新方式,以克服二进制粒子群算法易陷入局部极值的缺陷。同时,采用精英档案策略动态维护最优解集,并加快算法收敛速度。仿真结果表明,多目标二进制粒子群任务分配算法可以有效缩短任务处理周期,并且可以大幅度降低网络节点能耗。(3)分阶段的欺骗攻击源定位方法的研究。分阶段的欺骗攻击定位方法包括离线采集阶段,粗定位阶段,以及精确定位阶段。离线采集阶段建立轻量级的指纹数据库。基于定位任务分配方案,粗定位阶段利用轻量级的指纹数据库或者使用三边定位方法对攻击节点进行粗略定位。精确定位阶段采用改进的粒子群优化算法迭代优化定位结果。仿真结果表明,该分阶段的欺骗攻击定位方法在粗定位阶段使用的三边定位法可以减少信标节点的计算量,后期精确定位阶段采用改进粒子群定位算法可以改善粒子群算法易陷入局部极值的缺陷,从而实现低能耗、高精度定位欺骗攻击节点的目标。
[Abstract]:Wireless sensor networks face more security threats than traditional wired and wireless networks due to their own characteristics. Spoofing attack is one of the most difficult problems in the security field.In recent years, scholars at home and abroad have studied attack detection and attack localization, and obtained some research results. However, the research of spoofing attack in wireless sensor networks is still a challenging problem, which needs further research.Therefore, this paper studies a series of scientific problems derived from spoofing attacks, such as spoofing attack detection, spoofing attack source localization and so on, and obtains some new algorithms for spoofing attack localization in wireless sensor networks.The main research work of this paper includes: (1) A K-means spoofing attack detection model based on improved particle swarm optimization algorithm .KIPSO spoofing attack detection model describes spoofing attack detection as a statistical hypothesis test.Based on the correlation between the received signal strength and the physical location, the difference of the received signal intensity at different locations is used to detect the attack.In the attack detection stage, the KIPSO clustering algorithm is first used to cluster the received signal strength, so as to calculate the distance between the cluster centers, and finally determine whether the node is spoofed by the threshold detection.The simulation results show that the KIPSO spoofing attack detection model can effectively solve the problem of K-means algorithm falling into local extremum while improving the detection rate and the reliability of alarm.Based on the total task time, total energy consumption and load balance, the cost function is established in the multi-target binary particle swarm attack localization task allocation algorithm. Under the constraints of workload and received signal intensity space, the cost function can be obtained under the condition of the total task completion time, the total energy consumption and the load balance degree.In order to overcome the defect of binary particle swarm optimization (BPSO) algorithm which is easy to fall into local extremum the inertia weight of MOBPSOTA algorithm is updated in nonlinear way.At the same time, the elite file strategy is used to dynamically maintain the optimal solution set and speed up the convergence of the algorithm.Simulation results show that the multi-objective binary particle swarm optimization algorithm can effectively shorten the task processing period and greatly reduce the network node energy consumption.The spoofing attack localization method consists of off-line acquisition stage, coarse location stage and accurate location stage.A lightweight fingerprint database is established at the off-line acquisition stage.Based on the localization task assignment scheme, rough location of attack nodes is carried out by using a lightweight fingerprint database or a trilateral location method in rough location stage.The improved particle swarm optimization (PSO) algorithm is used to optimize the localization results in the precise location stage.The simulation results show that the trilateral localization method used in rough localization can reduce the computation of beacon nodes.The improved particle swarm optimization (PSO) algorithm can improve the defect of PSO which is easy to fall into the local extremum in the later precise localization stage, thus achieving low energy consumption and high precision localization of the target of spoofing attack node.
【学位授予单位】：江南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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