The Research Of Spoofing Attacks' Localization Technology In Wireless Sensor Network

The characteristics of wireless sensor network(WSN)make it face more security threats than the traditional wired and wireless network.Spoofing attack is one of the key problems to be overcome in WSN's security field.In recent years,domestic and foreign scholars have conducted researches on attack detection and attack localization and obtained some research achievements.But spoofing attack in wireless sensor networks is still a challenging problem,which needs to be further studied.Therefore,this paper studies a series of scientific problems derived from spoofing attacks,and designs some novel algorithms to apply in the technology of attack localization.The main research work of this paper includes:(1)Study on a K-means spoofing attack detection model based on Improved Particle Swarm Optimization(KIPSO).The KIPSO model formulates the spoofing attack detection as a statistical hypothesis testing and detects attacks by taking advantage of the received signal strength(RSS)differences between locations,for there are correlation between the physical location and the RSS.In attack detection phase,KIPSO algorithm is used to cluster the RSS for calculating the distance between two medoids.Eventually,judging whether spoofing attack exist or not via the trained threshold.Simulation results indicate that KIPSO spoofing attack detection model can improve detection rate and strengthen the alarm credibility,meanwhile,effectively solve the problem of K-means algorithm trapped in local optimum.(2)Study on a Multi-Objective Binary Particle Swarm Optimization Task Allocation(MOBPSOTA)algorithm.To realize the multi-objective optimization task allocation,cost functions of MOBPSOTA consist of total task execution time,total energy consumption and load balance,and the constraints consist of the work load and the RSS space constraints.Nonlinear decreasing inertia weight is adopted to overcome the drawback of easily trapping in local optimum in binary particle swarm optimization(BPSO)algorithm.Furthermore,to dynamically maintain the optimal solutions and improve the convergence rate,elite archive strategy is adopted.Simulation results indicate that MOBPSOTA can not only shorten the period of task processing,but also greatly reduce the energy consumption of the network.(3)Study on a Segmented Spoofing Attack Localization Method(SSALM).SSALM consists of offline collection stage,coarse localization stage and accurate localization stage.At the offline collection stage,the lightweight fingerprint database is established.At the coarse localization stage,the attack nodes are roughly located by using lightweight fingerprint database or trilateration method based on the localization task allocation solution.At the accurate localization stage,the coarse location results are iteratively optimized by Improved Particle Swarm Optimization(IPSO)algorithm.Simulation results show that SSALM can locate attackers with low energy consumption and high localization accuracy through the use of trilateration method for reducing the computational work and the use of IPSO localization algorithm for solving the problem of being trapped in local optimum with PSO algorithm.