基于核方法的網(wǎng)絡(luò)入侵檢測(cè)若干研究
[Abstract]:With the increasing popularity of the network, the problem of network security is becoming more and more prominent. How to ensure the security of the network system has become an urgent problem to be solved. The combination of kernel principal component analysis (KPCA),) particle swarm optimization (PSO) and support vector machine (SVM) in intrusion detection not only solves the redundancy of data information, but also avoids the blindness of SVM parameter selection. Further improve the performance of intrusion detection. This paper mainly studies the application of kernel method in intrusion detection. Firstly, the KPCA algorithm is studied and analyzed, and a hybrid kernel principal component analysis algorithm is proposed. Secondly, the parameter selection of SVM is studied, and two new optimization algorithms are proposed on the basis of PSO algorithm. The main innovations of this paper are as follows: (1) A MKPCA (Multiple Kernel Principal Component Analysis,MKPCA algorithm based on hybrid kernel function is proposed. In this algorithm, the feature extraction of intrusion detection data is carried out, and the dimension of the data is reduced under the condition of ensuring the integrity of the data information. The kernel function of the MKPCA algorithm proposed in this paper is not a single kernel, but combines the binomial kernel function of the global kernel function (multinomial kernel function) and the local kernel function (Gao Si kernel function) to improve the nonlinear feature extraction ability of KPCA (Kernel PrincipalComponent Analysis, MKPCA). Through the experiment of MKPCA feature extraction, it can be seen that the correctness of classification of the original data after feature extraction is improved, and the training and testing speed of the data is accelerated at the same time. (2) an intrusion detection algorithm based on dynamic particle swarm optimization (SVM (Dynamic Particle Swarm Optimization-SupportVector Machine, DPSO-SVM) is proposed. The dynamic inertia weight function and acceleration factor function are introduced to enhance the search ability of PSO algorithm and balance the global search ability and local search ability of PSO algorithm. The algorithm is applied to the parameter optimization of SVM. In this paper, the DPSO-SVM algorithm is used to classify the intrusion data processed by MKPCA. The results show that the algorithm improves the accuracy of classification and accelerates the convergence speed of the algorithm to the optimal solution. (3) A SVM (Dynamic Chaos Particle Swarm Optimization-Support Vector Machine, DCPSO-SVM intrusion detection algorithm based on dynamic chaotic particle swarm optimization is proposed. This method combines chaotic search with the dynamic particle swarm optimization algorithm proposed in this paper. DCPSO not only dynamically selects the main parameters of PSO algorithm, but also improves the diversity of population and ergodicity of particle search, and further improves the accuracy of PSO algorithm. Convergence rate. Through the experiment of DCPSO-SVM in intrusion detection, it can be seen that the classification accuracy and convergence efficiency of the algorithm have been improved.
【學(xué)位授予單位】:南京郵電大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2014
【分類號(hào)】:TP393.08
【相似文獻(xiàn)】
相關(guān)期刊論文 前10條
1 經(jīng)玲,朱甫芹,魯緋,孫君社;腐乳感官和理化品質(zhì)的核主成分分析[J];中國(guó)農(nóng)業(yè)大學(xué)學(xué)報(bào);2004年03期
2 趙麗紅;孫宇舸;蔡玉;徐心和;;基于核主成分分析的人臉識(shí)別[J];東北大學(xué)學(xué)報(bào);2006年08期
3 薛寧?kù)o;;基于測(cè)地距離的核主成分分析方法[J];微計(jì)算機(jī)信息;2010年31期
4 劉嵩;羅敏;張國(guó)平;;基于對(duì)稱核主成分分析的人臉識(shí)別[J];計(jì)算機(jī)應(yīng)用;2012年05期
5 何振學(xué);張貴倉(cāng);譙鈞;楊林英;;對(duì)稱核主成分分析及其在人臉識(shí)別中的應(yīng)用[J];計(jì)算機(jī)工程;2013年03期
6 萬星火;金永超;鄭俊玲;;基于核主成分分析的環(huán)境質(zhì)量綜合評(píng)價(jià)模型[J];電腦知識(shí)與技術(shù);2014年09期
7 殷俊;周靜波;金忠;;基于余弦角距離的主成分分析與核主成分分析[J];計(jì)算機(jī)工程與應(yīng)用;2011年03期
8 孟凡榮;楊開睿;梁志貞;;魯棒的加權(quán)核主成分分析算法[J];計(jì)算機(jī)應(yīng)用研究;2013年07期
9 劉權(quán);郭武;;基于核主成分分析的話題跟蹤系統(tǒng)[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年06期
10 賈亞瓊;;基于核主成分分析的圖像去噪[J];科學(xué)技術(shù)與工程;2009年19期
相關(guān)會(huì)議論文 前3條
1 薛永剛;朱靖波;魏剛;;基于核主成分分析的文本分類[A];第二屆全國(guó)信息檢索與內(nèi)容安全學(xué)術(shù)會(huì)議(NCIRCS-2005)論文集[C];2005年
2 劉權(quán);郭武;;基于核主成分分析的話題跟蹤系統(tǒng)[A];第十二屆全國(guó)人機(jī)語音通訊學(xué)術(shù)會(huì)議(NCMMSC'2013)論文集[C];2013年
3 徐揚(yáng);陳實(shí);田玉敏;;基于核主成分分析的步態(tài)識(shí)別[A];2008'中國(guó)信息技術(shù)與應(yīng)用學(xué)術(shù)論壇論文集(二)[C];2008年
相關(guān)碩士學(xué)位論文 前10條
1 馬文青;一種加權(quán)核主成分分析及其相關(guān)參數(shù)的選取[D];大連海事大學(xué);2009年
2 劉素京;基于核主成分分析和支持向量機(jī)的飛機(jī)艙音信號(hào)的識(shí)別[D];南京航空航天大學(xué);2009年
3 賈亞瓊;基于核主成分分析的圖像降噪方法研究[D];華南理工大學(xué);2010年
4 萬康康;基于核主成分分析的原像問題研究[D];南京理工大學(xué);2014年
5 孫宗寶;基于軟間隔支持向量機(jī)和核主成分分析的入侵檢測(cè)研究[D];哈爾濱理工大學(xué);2007年
6 王輝;基于核主成分分析特征提取及支持向量機(jī)的人臉識(shí)別應(yīng)用研究[D];合肥工業(yè)大學(xué);2006年
7 沈徐輝;基于核主成分與支持向量機(jī)的體內(nèi)藥物代謝預(yù)測(cè)[D];浙江大學(xué);2011年
8 趙春標(biāo);工業(yè)經(jīng)濟(jì)監(jiān)測(cè)預(yù)測(cè)模型的研究與應(yīng)用[D];合肥工業(yè)大學(xué);2012年
9 張立;基于核主成分分析和多重分形的地球化學(xué)綜合異常信息提取[D];成都理工大學(xué);2014年
10 劉孔源;基于核方法的網(wǎng)絡(luò)入侵檢測(cè)若干研究[D];南京郵電大學(xué);2014年
本文編號(hào):2475304
本文鏈接:http://www.lk138.cn/guanlilunwen/ydhl/2475304.html