本文關(guān)鍵詞： 決策樹 小波變換 網(wǎng)絡(luò)流量　出處：《科技通報(bào)》2014年06期 　論文類型：期刊論文

【摘要】：為有效定位識別和提取網(wǎng)絡(luò)流量序列的暫態(tài)性異常特征,針對網(wǎng)絡(luò)異常流量特征擾動性和暫態(tài)性特點(diǎn),提出一種基于小波分解的二叉分類回歸決策樹主分量特征優(yōu)化跟蹤特征提取算法。利用訓(xùn)練集建立決策樹模型,采用二叉分類回歸決策樹模型進(jìn)行主分量特征優(yōu)化跟蹤建模,利用雙正交提升小波分解得到的各層細(xì)節(jié)信號對暫態(tài)性擾動特征的敏感性,實(shí)現(xiàn)網(wǎng)絡(luò)流量異常特征的定位提取和識別。仿真實(shí)驗(yàn)表明,改進(jìn)算法的抗干擾能力和分辨率提高顯著,暫態(tài)性異常特征譜圖分辨能力提高,異常特征分布譜清晰可見,展示了較好的特征提取和狀態(tài)識別性能。
[Abstract]:In order to identify and extract the transient anomaly characteristics of network traffic sequence effectively, the characteristic disturbance and transient characteristics of network abnormal traffic are analyzed. This paper presents a feature extraction algorithm based on wavelet decomposition for feature optimization of principal component of binary classification regression decision tree. A decision tree model is established by using training set, and a binary classification regression decision tree model is used to model the optimal tracking of principal component feature. Using biorthogonal lifting wavelet decomposition, the sensitivity of each layer of detail signal to transient disturbance feature is obtained, and the location extraction and recognition of network traffic anomaly feature are realized. The simulation results show that, The improved algorithm can improve the anti-jamming ability and resolution, improve the resolution of transient anomaly feature spectrum, and the spectrum of abnormal feature distribution is clearly visible, which shows the better performance of feature extraction and state recognition.
【作者單位】： 平頂山學(xué)院計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【分類號】：TP393.06

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