發(fā)布時間：2018-06-24 23:30

  本文選題：大數(shù)據(jù)網(wǎng)絡 + 入侵過程��； 參考：《科學技術與工程》2016年14期

【摘要】：大數(shù)據(jù)網(wǎng)絡數(shù)據(jù)規(guī)模巨大,對入侵過程痕跡數(shù)據(jù)進行監(jiān)測的效率通常較低,一些帶有入侵痕跡的數(shù)據(jù)特征在大數(shù)據(jù)環(huán)境下,特征逐漸淡化,當前方法無法在淡化的情況下準確采集痕跡數(shù)據(jù)的特點,無法形成待監(jiān)測數(shù)據(jù)與痕跡數(shù)據(jù)之間的關系,導致監(jiān)測效率和精度低下。提出一種基于模糊聚類概率的大數(shù)據(jù)網(wǎng)絡入侵過程的痕跡數(shù)據(jù)監(jiān)測方法,將采集的痕跡數(shù)據(jù)轉(zhuǎn)換成頻域信號,對其進行頻譜或功率譜分析,依據(jù)時間變化的幅值將其轉(zhuǎn)換成隨頻率變化的功率。采用核主元分析對痕跡數(shù)據(jù)信號特征進行提取,利用非線性轉(zhuǎn)換將樣本痕跡數(shù)據(jù)信號從輸入空間映射至高維特征空間,在高維特征空間中通過PCA進行痕跡數(shù)據(jù)信號的頻域特征提取。構(gòu)建一個數(shù)學模型對特征模糊聚類概率進行描述,對待監(jiān)測數(shù)據(jù)和痕跡數(shù)據(jù)之間的特征模糊聚類概率進行計算,通過衡量理論進行對比分析,使大數(shù)據(jù)網(wǎng)絡入侵過程中的痕跡數(shù)據(jù)被完整的監(jiān)測。實驗結(jié)果表明,所提方法不僅所需時間少,而且監(jiān)測精度高。
[Abstract]:Because of the large scale of big data network data, the efficiency of monitoring intrusion trace data is usually low. Some data features with intrusion trace are gradually desalinated in big data environment. The current method can not accurately collect trace data under the condition of desalination, and can not form the relationship between the monitoring data and trace data, which leads to the low efficiency and precision of monitoring. A method of trace data monitoring in big data network intrusion process based on fuzzy clustering probability is proposed. The trace data collected is converted into frequency domain signal, and the spectrum or power spectrum is analyzed. It is converted to power varying with frequency according to the amplitude of time variation. The feature of trace data signal is extracted by kernel principal component analysis, and the sample trace data signal is mapped from input space to high dimensional feature space by nonlinear transformation. The feature extraction of trace data signal in frequency domain is carried out by PCA in high dimensional feature space. A mathematical model is constructed to describe the feature fuzzy clustering probability and to calculate the feature fuzzy clustering probability between the monitoring data and trace data. The trace data in the process of big data network intrusion is monitored completely. The experimental results show that the proposed method not only requires less time, but also has high monitoring accuracy.
【作者單位】： 重慶市氣象局;
【分類號】：TP393.08

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本文編號：2063473