發(fā)布時(shí)間：2018-04-12 16:30

  本文選題：入侵檢測系統(tǒng) + 非平衡數(shù)據(jù)��； 參考：《鄭州大學(xué)》2014年碩士論文

【摘要】：隨著計(jì)算機(jī)網(wǎng)絡(luò)的爆炸式發(fā)展，，如何保障網(wǎng)絡(luò)安全成為人們亟需解決的問題。入侵檢測系統(tǒng)在網(wǎng)絡(luò)安全方面發(fā)揮的積極作用使它成為人們關(guān)注和研究的焦點(diǎn)之一。雖然人們已經(jīng)將數(shù)據(jù)挖掘和模式識(shí)別算法應(yīng)用到了入侵檢測領(lǐng)域，但是效果并不理想。因?yàn)槿肭謾z測系統(tǒng)的輸入是非平衡數(shù)據(jù)，與傳統(tǒng)分類器不同，入侵檢測數(shù)據(jù)的少數(shù)類樣本才是人們關(guān)注的核心。傳統(tǒng)分類器和性能評(píng)估指標(biāo)是針對(duì)平衡數(shù)據(jù)集的，通過預(yù)處理使數(shù)據(jù)平衡化是入侵檢測系統(tǒng)有效運(yùn)行的關(guān)鍵。 KDD Cup99數(shù)據(jù)集是本文仿真實(shí)驗(yàn)采用的數(shù)據(jù)集。針對(duì)數(shù)據(jù)不平衡的問題，本文對(duì)經(jīng)典SMOTE過抽樣算法進(jìn)行改進(jìn)；針對(duì)入侵檢測數(shù)據(jù)高維度的特點(diǎn)，應(yīng)用基于信息增益的特征選擇算法和面向目標(biāo)變量的主成分分析算法對(duì)數(shù)據(jù)降維。最后，采用了傳統(tǒng)的貝葉斯分類器對(duì)平衡降維后的數(shù)據(jù)進(jìn)行分類操作。針對(duì)入侵檢測數(shù)據(jù)非平衡的特點(diǎn)，本文實(shí)驗(yàn)綜合參考檢測率、誤報(bào)率、G-means和整體準(zhǔn)確率四個(gè)指標(biāo)來分析評(píng)價(jià)入侵檢測系統(tǒng)的性能。實(shí)驗(yàn)仿真結(jié)果表明，提出的預(yù)處理方案可在維持較低誤報(bào)率的情況下有效提高入侵檢測系統(tǒng)的檢測率和整體準(zhǔn)確率。
[Abstract]:With the explosive development of computer network, how to ensure network security becomes an urgent problem.Intrusion detection system (IDS) plays an active role in network security, which makes it one of the focus of attention and research.Although data mining and pattern recognition algorithms have been applied to intrusion detection, the results are not satisfactory.Because the input of intrusion detection system is unbalanced data, different from the traditional classifier, a few kinds of samples of intrusion detection data are the core of people's attention.The traditional classifier and performance evaluation index are aimed at the balanced data set. The key to the effective operation of the intrusion detection system is to balance the data by preprocessing.KDD Cup99 data set is the data set used in the simulation experiment in this paper.Aiming at the problem of data imbalance, this paper improves the classical SMOTE over-sampling algorithm, aiming at the characteristics of high-dimensional intrusion detection data.The feature selection algorithm based on information gain and the principal component analysis (PCA) algorithm for target variables are used to reduce the dimension of data.Finally, the traditional Bayesian classifier is used to classify the data after balanced dimensionality reduction.Aiming at the characteristics of non-equilibrium intrusion detection data, this paper analyzes and evaluates the performance of intrusion detection system by synthesizing four indexes: reference detection rate, false alarm rate G-means and overall accuracy.The experimental results show that the proposed preprocessing scheme can effectively improve the detection rate and the overall accuracy of the intrusion detection system under the condition of maintaining a low false alarm rate.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08

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本文編號(hào)：1740529