本文選題：無線傳感器網(wǎng)絡(luò)　切入點(diǎn)：攻擊檢測　出處：《江南大學(xué)》2017年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)本身的特性使其面臨比傳統(tǒng)有線和無線網(wǎng)絡(luò)更多的安全威脅,欺騙攻擊則是其安全領(lǐng)域必須攻克的難題之一。近些年來,國內(nèi)外學(xué)者對攻擊檢測及攻擊定位進(jìn)行了研究,并獲得了一些研究成果,但是無線傳感器網(wǎng)絡(luò)中的欺騙攻擊研究仍然是一個挑戰(zhàn)性的問題,有待進(jìn)一步深入研究。因此,本文研究由欺騙攻擊所衍生的一系列科學(xué)問題,如欺騙攻擊檢測、欺騙攻擊源定位等,得到了一些新算法應(yīng)用于無線傳感器網(wǎng)絡(luò)欺騙攻擊定位技術(shù)中。本文的主要研究工作包括:(1)基于改進(jìn)粒子群優(yōu)化算法的K均值欺騙攻擊檢測模型的研究。KIPSO欺騙攻擊檢測模型將欺騙攻擊檢測描述為一個統(tǒng)計假設(shè)檢驗(yàn),基于接收信號強(qiáng)度與物理位置的相關(guān)性,利用不同位置節(jié)點(diǎn)接收信號強(qiáng)度的差異進(jìn)行攻擊檢測。攻擊檢測階段,首先使用KIPSO聚類算法對接收信號強(qiáng)度進(jìn)行聚類分析,從而計算類中心之間的距離,最終通過閾值檢測判斷節(jié)點(diǎn)是否受到欺騙攻擊。仿真結(jié)果表明,KIPSO欺騙攻擊檢測模型能在提高檢出率、增強(qiáng)報警可信度的同時,有效解決K均值算法陷入局部極值的問題。(2)多目標(biāo)二進(jìn)制粒子群攻擊定位任務(wù)分配算法的研究。多目標(biāo)二進(jìn)制粒子群攻擊定位任務(wù)分配算法根據(jù)任務(wù)完成總時間、總能量消耗和負(fù)載平衡度來建立代價函數(shù),在工作負(fù)荷和接收信號強(qiáng)度空間約束條件下,完成多目標(biāo)優(yōu)化的任務(wù)分配工作。MOBPSOTA算法中的慣性權(quán)重采取非線性的更新方式,以克服二進(jìn)制粒子群算法易陷入局部極值的缺陷。同時,采用精英檔案策略動態(tài)維護(hù)最優(yōu)解集,并加快算法收斂速度。仿真結(jié)果表明,多目標(biāo)二進(jìn)制粒子群任務(wù)分配算法可以有效縮短任務(wù)處理周期,并且可以大幅度降低網(wǎng)絡(luò)節(jié)點(diǎn)能耗。(3)分階段的欺騙攻擊源定位方法的研究。分階段的欺騙攻擊定位方法包括離線采集階段,粗定位階段,以及精確定位階段。離線采集階段建立輕量級的指紋數(shù)據(jù)庫�；诙ㄎ蝗蝿�(wù)分配方案,粗定位階段利用輕量級的指紋數(shù)據(jù)庫或者使用三邊定位方法對攻擊節(jié)點(diǎn)進(jìn)行粗略定位。精確定位階段采用改進(jìn)的粒子群優(yōu)化算法迭代優(yōu)化定位結(jié)果。仿真結(jié)果表明,該分階段的欺騙攻擊定位方法在粗定位階段使用的三邊定位法可以減少信標(biāo)節(jié)點(diǎn)的計算量,后期精確定位階段采用改進(jìn)粒子群定位算法可以改善粒子群算法易陷入局部極值的缺陷,從而實(shí)現(xiàn)低能耗、高精度定位欺騙攻擊節(jié)點(diǎn)的目標(biāo)。
[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.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

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