本文選題：無線傳感器網(wǎng)絡(luò)　切入點(diǎn)：支持向量機(jī)　出處：《中國(guó)計(jì)量學(xué)院》2014年碩士論文

【摘要】：近些年來，網(wǎng)絡(luò)入侵方法層出不窮，而對(duì)節(jié)點(diǎn)能量和處理能力有限的無線傳感器網(wǎng)絡(luò)而言，其入侵手段更是防不勝防。針對(duì)WSN中常出現(xiàn)的Hello洪泛攻擊、黑洞攻擊、選擇性轉(zhuǎn)發(fā)攻擊、DoS攻擊和Sybil攻擊，本文提出了基于聚類與SVM多分類的糾錯(cuò)輸出編碼算法，該算法可以在較低時(shí)間復(fù)雜度的基礎(chǔ)上有效地檢測(cè)出以上攻擊中的兩種，為攻擊的誤用檢測(cè)提供了有效的途徑。本文所做的工作以及研究成果如下： （1）在構(gòu)造改進(jìn)型H-ECOC-SVM糾錯(cuò)輸出編碼矩陣時(shí)，同時(shí)引入了Hadamard編碼和稀疏型隨機(jī)編碼兩種思想，為了增強(qiáng)編碼矩陣的可用性和入侵檢測(cè)的準(zhǔn)確性，在對(duì)編碼矩陣進(jìn)行構(gòu)造時(shí)考慮到了各列間的相關(guān)性以及各行間的漢明距離等影響因素，使各列間互不相關(guān)、各行間的最小漢明距離盡可能最大，來滿足SVM分類器的訓(xùn)練要求，為構(gòu)建最優(yōu)SVM二分類器奠定了良好的理論基礎(chǔ)。 （2）在分類器的構(gòu)建方面，采用網(wǎng)格搜索和五折交叉驗(yàn)證法進(jìn)行核參數(shù)和懲罰參數(shù)的求取，并根據(jù)H-ECOC-SVM矩陣的編碼規(guī)則，把一個(gè)多類分類問題分解為多個(gè)兩類問題來進(jìn)行求解，，這種方法不僅減小了需要求取的分類器參數(shù)的個(gè)數(shù)，而且簡(jiǎn)化了單個(gè)分類器的訓(xùn)練模型，為多類攻擊的檢測(cè)帶來了較大的便利。 （3）在特征提取之前，首先使用聚類算法對(duì)測(cè)試數(shù)據(jù)集進(jìn)行一個(gè)初始的攻擊檢測(cè)，在不存在攻擊的情況下，該方法節(jié)省了一定的時(shí)間和能量消耗。PCA分析法在對(duì)訓(xùn)練和測(cè)試數(shù)據(jù)進(jìn)行主成分分析時(shí)，對(duì)特征向量的數(shù)據(jù)維數(shù)進(jìn)行了分析提取，該過程減少了分類器的運(yùn)算時(shí)間和工作量，滿足了入侵檢測(cè)對(duì)時(shí)間復(fù)雜度的要求。 （4）對(duì)Hello洪泛攻擊、黑洞攻擊、選擇性轉(zhuǎn)發(fā)攻擊、DoS攻擊和Sybil攻擊進(jìn)行檢測(cè)時(shí)，實(shí)現(xiàn)了三種攻擊的檢測(cè)率在90%以上，兩種攻擊的漏報(bào)率在5%以下，檢測(cè)時(shí)間代價(jià)平均維持在0.1s以下的檢測(cè)水平，在有效地進(jìn)行WSN入侵檢測(cè)中具有一定的實(shí)際參考價(jià)值。
[Abstract]:In recent years, network intrusion methods emerge in endlessly, but for wireless sensor networks with limited node energy and processing capacity, the intrusion means are even more difficult to prevent.Aiming at Hello flooding attack, black hole attack, selective forward attack dos attack and Sybil attack in WSN, this paper proposes an error correction output coding algorithm based on clustering and SVM multi-classification.This algorithm can effectively detect two of the above attacks on the basis of low time complexity, which provides an effective way for the misuse detection of attacks.The work and results of this paper are as follows:In order to enhance the usability of coding matrix and the accuracy of intrusion detection, two ideas of Hadamard coding and sparse random coding are introduced in the construction of improved H-ECOC-SVM error correction output coding matrix.In order to meet the training requirements of SVM classifier, the correlation of each column and the hamming distance between rows are taken into account in the construction of the coding matrix.It lays a good theoretical foundation for constructing the optimal SVM binary classifier.In the construction of classifier, the kernel parameters and penalty parameters are obtained by grid search and 50% cross-validation. According to the coding rules of H-ECOC-SVM matrix, a multi-class classification problem is decomposed into two kinds of problems to solve.This method not only reduces the number of classifier parameters to be obtained, but also simplifies the training model of a single classifier, which makes the detection of multi-class attacks more convenient.(3) before feature extraction, the clustering algorithm is used to detect the initial attack on the test data set.This method saves a certain amount of time and energy consumption. PCA method can analyze and extract the dimension of the feature vector when the training and test data are analyzed by principal component analysis (PCA). The process reduces the operation time and workload of the classifier.The time complexity of intrusion detection is satisfied.In the detection of Hello flooding attack, black hole attack, selective forward attack dos attack and Sybil attack, the detection rate of three attacks is over 90%, and the missing rate of two attacks is less than 5%.The detection time cost is kept below 0.1 s on average, so it has some practical reference value in effective WSN intrusion detection.
【學(xué)位授予單位】：中國(guó)計(jì)量學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;TP393.08

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本文編號(hào)：1727248