本文選題：無線傳感器網(wǎng)絡(luò)　切入點：入侵檢測　出處：《江南大學(xué)》2014年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Network, WSN)作為一種新型信息獲取技術(shù)，已逐漸成為在國際上備受關(guān)注的、由多學(xué)科相互交叉融合的新型前沿研究領(lǐng)域。其傳感器節(jié)點數(shù)量巨大、部署在無人區(qū)域、多跳通信易受竊聽和干擾、分布性、自組織等自身特性更是使得WSN的安全研究面臨著巨大的挑戰(zhàn)。本文主要研究WSN入侵檢測技術(shù)，分析了網(wǎng)絡(luò)中存在的主要威脅與攻擊，選擇和提取相應(yīng)的特征作為檢測特征，提出了一種分級混合入侵檢測算法；并將人工智能領(lǐng)域的最新成果——人工免疫系統(tǒng)引入到WSN入侵檢測中來，提出了兩種基于免疫算法的入侵檢測算法。 (1) WSN分級混合入侵檢測算法。構(gòu)建WSN基站級-簇級的兩級入侵檢測模型。采用主成分分析法進行特征降維，降低數(shù)據(jù)存儲量和計算量；簇級中普通節(jié)點采用基于K近鄰直推式信度機進行異常檢測，簇頭采用粒子群優(yōu)化參數(shù)的支持向量機對檢測到的異常進行進一步誤用檢測分類，，保障簇內(nèi)節(jié)點安全；基站級將異常檢測技術(shù)與誤用檢測技術(shù)相結(jié)合，處理簇頭提交的監(jiān)測數(shù)據(jù)，可同時提高檢出率和降低誤報率，保障簇頭安全。 (2)基于改進V-detector的WSN入侵檢測算法。充分利用基站資源不受限的特點進行訓(xùn)練樣本的選取和檢測器的生成及優(yōu)化；普通節(jié)點負責(zé)數(shù)據(jù)的采集與特征的選取；設(shè)立專門的檢測節(jié)點，對選取的特征進行降維，并先后采用記憶檢測器集和成熟檢測器集進行兩級入侵檢測。從訓(xùn)練樣本的篩選、檢測器的生成規(guī)則、檢測器的優(yōu)化算法以及檢測階段的檢測規(guī)則四個方面對V-detector算法進行補充和改進，使其適用于能量有限的無線傳感器網(wǎng)絡(luò)。 (3)基于粗糙集和改進樹突狀細胞算法(Dendritic Cell Algorithm, DCA)的WSN異常檢測算法。基于生物免疫系統(tǒng)原理，構(gòu)架無線傳感器網(wǎng)絡(luò)異常檢測框架：依據(jù)粗糙集屬性約簡理論進行信號降維，以減少數(shù)據(jù)存儲量和計算量，同時設(shè)定異常檢測輸入信號選取機制；改進基于免疫危險理論的DCA，設(shè)定淋巴結(jié)樹突狀細胞(Dendritic Cell, DC)容量并引入DC更新機制，在降低節(jié)點數(shù)據(jù)存儲量的同時保證了DC的新鮮性；將遷移閾值由固定取值改為區(qū)域取值，降低節(jié)點通信能耗；并修改了抗原異常評判標準，將靜態(tài)抗原異常值變?yōu)閯討B(tài)，實時描述網(wǎng)絡(luò)的動態(tài)異常程度。 仿真結(jié)果表明：主成分分析方法與粗糙集屬性約簡方法均能達到較好的降維效果；分級混合檢測算法能在小樣本情況下同時降低虛警率與漏警率；基于V-detector的檢測算法能降低數(shù)據(jù)存儲量和計算量，提高檢測率，并能快速應(yīng)對二次進攻；基于改進DCA的檢測算法能實時檢測網(wǎng)絡(luò)異常并具有較高的檢測正確率。
[Abstract]:Wireless sensor network (Wireless Sensor Network, WSN) as a new information acquisition technology, has gradually become a concern in the world, a new frontier research field from several disciplines. The huge number of sensor nodes, deployed in unattended, multi hop communication is vulnerable to eavesdropping and jamming, distributed, self the organization's own characteristics is the research on the security of the WSN faces a great challenge. This paper mainly studies the WSN intrusion detection technology, analyzes the main threats and attacks in the network, select and extract the corresponding feature as the feature detection, we propose a hierarchical hybrid intrusion detection algorithm; and the latest achievements in the field of artificial intelligence the artificial immune system is introduced into the WSN intrusion detection system, put forward two kinds of intrusion detection algorithm based on immune algorithm.
(1) WSN hybrid intrusion detection algorithm. Constructed the two level intrusion detection model WSN base station level - cluster level. Using principal component analysis method for feature reduction, reduce the data storage and calculation; ordinary node cluster level based on K nearest neighbor transductive reliability for anomaly detection, cluster head by anomaly the detection of the support vector machine and particle swarm optimization parameters further misuse detection and classification, to ensure the safety of the node in the cluster; the base station level anomaly detection and misuse detection technology combined with the process of monitoring data submitted by the cluster head, which can improve the detection rate and reduce the false alarm rate, guarantee the cluster head safety.
(2) improved intrusion detection algorithm based on V-detector WSN. The generation and optimization of full use of characteristics of restricted base resources for the selection of training samples and detectors; the ordinary node is responsible for data acquisition and feature; the establishment of specialized detection node, to reduce the dimensionality of feature selection, and has the memory detector set and the mature detector set two level intrusion detection. From the selection of training samples, generating rules of the detector, supplement and improvement of V-detector algorithm in four aspects of detection rule optimization algorithm detector and detection stage, which is suitable for energy limited wireless sensor networks.
(3) the rough set and the improved algorithm based on dendritic cells (Dendritic Cell Algorithm, DCA) anomaly detection algorithm WSN. Based on the principle of biological immune system, anomaly detection framework architecture of wireless sensor network: Based on rough set attribute reduction theory of signal reduction, to reduce the amount of data storage and computation, while setting the anomaly detection input signal selection mechanism; Improved Immune Danger Theory Based on DCA, set the lymph node dendritic cell (Dendritic Cell, DC capacity) and the introduction of DC update mechanism, while reducing the node data storage to ensure the freshness of DC; will migrate from fixed value to the threshold value of region, reduce energy consumption and change; the abnormal antigen evaluation standard, static antigen abnormal value into dynamic, real-time dynamic description of the network. The degree of abnormality
The simulation results show that the method of principal component analysis and rough set attribute reduction method can achieve a better effect of dimension reduction; hierarchical hybrid detection algorithm can also reduce the false alarm rate and false alarm rate in the case of small samples; V-detector detection algorithm can reduce the amount of data storage and computation based on and can improve the detection rate. A rapid response to the two attack; improved DCA detection algorithm can correct detection rate of real-time network anomaly detection and has high based.

【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】：2014
【分類號】：TP212.9;TN929.5;TP274

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